The modulation of outdoor running speed : the influence of gradient

This thesis aimed to investigate the way in which distance runners modulate their speed in an effort to understand the key processes and determinants of speed selection when encountering hills in natural outdoor environments. One factor which has limited the expansion of knowledge in this area has been a reliance on the motorized treadmill which constrains runners to constant speeds and gradients and only linear paths. Conversely, limits in the portability or storage capacity of available technology have restricted field research to brief durations and level courses. Therefore another aim of this thesis was to evaluate the capacity of lightweight, portable technology to measure running speed in outdoor undulating terrain. The first study of this thesis assessed the validity of a non-differential GPS to measure speed, displacement and position during human locomotion. Three healthy participants walked and ran over straight and curved courses for 59 and 34 trials respectively. A non-differential GPS receiver provided speed data by Doppler Shift and change in GPS position over time, which were compared with actual speeds determined by chronometry. Displacement data from the GPS were compared with a surveyed 100m section, while static positions were collected for 1 hour and compared with the known geodetic point. GPS speed values on the straight course were found to be closely correlated with actual speeds (Doppler shift: r = 0.9994, p < 0.001, Δ GPS position/time: r = 0.9984, p < 0.001). Actual speed errors were lowest using the Doppler shift method (90.8% of values within ± 0.1 m.sec -1). Speed was slightly underestimated on a curved path, though still highly correlated with actual speed (Doppler shift: r = 0.9985, p < 0.001, Δ GPS distance/time: r = 0.9973, p < 0.001). Distance measured by GPS was 100.46 ± 0.49m, while 86.5% of static points were within 1.5m of the actual geodetic point (mean error: 1.08 ± 0.34m, range 0.69-2.10m). Non-differential GPS demonstrated a highly accurate estimation of speed across a wide range of human locomotion velocities using only the raw signal data with a minimal decrease in accuracy around bends. This high level of resolution was matched by accurate displacement and position data. Coupled with reduced size, cost and ease of use, the use of a non-differential receiver offers a valid alternative to differential GPS in the study of overground locomotion. The second study of this dissertation examined speed regulation during overground running on a hilly course. Following an initial laboratory session to calculate physiological thresholds (VO2 max and ventilatory thresholds), eight experienced long distance runners completed a self- paced time trial over three laps of an outdoor course involving uphill, downhill and level sections. A portable gas analyser, GPS receiver and activity monitor were used to collect physiological, speed and stride frequency data. Participants ran 23% slower on uphills and 13.8% faster on downhills compared with level sections. Speeds on level sections were significantly different for 78.4 ± 7.0 seconds following an uphill and 23.6 ± 2.2 seconds following a downhill. Speed changes were primarily regulated by stride length which was 20.5% shorter uphill and 16.2% longer downhill, while stride frequency was relatively stable. Oxygen consumption averaged 100.4% of runner’s individual ventilatory thresholds on uphills, 78.9% on downhills and 89.3% on level sections. Group level speed was highly predicted using a modified gradient factor (r2 = 0.89). Individuals adopted distinct pacing strategies, both across laps and as a function of gradient. Speed was best predicted using a weighted factor to account for prior and current gradients. Oxygen consumption (VO2) limited runner’s speeds only on uphill sections, and was maintained in line with individual ventilatory thresholds. Running speed showed larger individual variation on downhill sections, while speed on the level was systematically influenced by the preceding gradient. Runners who varied their pace more as a function of gradient showed a more consistent level of oxygen consumption. These results suggest that optimising time on the level sections after hills offers the greatest potential to minimise overall time when running over undulating terrain. The third study of this thesis investigated the effect of implementing an individualised pacing strategy on running performance over an undulating course. Six trained distance runners completed three trials involving four laps (9968m) of an outdoor course involving uphill, downhill and level sections. The initial trial was self-paced in the absence of any temporal feedback. For the second and third field trials, runners were paced for the first three laps (7476m) according to two different regimes (Intervention or Control) by matching desired goal times for subsections within each gradient. The fourth lap (2492m) was completed without pacing. Goals for the Intervention trial were based on findings from study two using a modified gradient factor and elapsed distance to predict the time for each section. To maintain the same overall time across all paced conditions, times were proportionately adjusted according to split times from the self-paced trial. The alternative pacing strategy (Control) used the original split times from this initial trial. Five of the six runners increased their range of uphill to downhill speeds on the Intervention trial by more than 30%, but this was unsuccessful in achieving a more consistent level of oxygen consumption with only one runner showing a change of more than 10%. Group level adherence to the Intervention strategy was lowest on downhill sections. Three runners successfully adhered to the Intervention pacing strategy which was gauged by a low Root Mean Square error across subsections and gradients. Of these three, the two who had the largest change in uphill-downhill speeds ran their fastest overall time. This suggests that for some runners the strategy of varying speeds systematically to account for gradients and transitions may benefit race performances on courses involving hills. In summary, a non – differential receiver was found to offer highly accurate measures of speed, distance and position across the range of human locomotion speeds. Self-selected speed was found to be best predicted using a weighted factor to account for prior and current gradients. Oxygen consumption limited runner’s speeds only on uphills, speed on the level was systematically influenced by preceding gradients, while there was a much larger individual variation on downhill sections. Individuals were found to adopt distinct but unrelated pacing strategies as a function of durations and gradients, while runners who varied pace more as a function of gradient showed a more consistent level of oxygen consumption. Finally, the implementation of an individualised pacing strategy to account for gradients and transitions greatly increased runners’ range of uphill-downhill speeds and was able to improve performance in some runners. The efficiency of various gradient-speed trade- offs and the factors limiting faster downhill speeds will however require further investigation to further improve the effectiveness of the suggested strategy.

Infinite-horizon policy-gradient estimation

Gradient-based approaches to direct policy search in reinforcement learning have received much recent attention as a means to solve problems of partial observability and to avoid some of the problems associated with policy degradation in value-function methods. In this paper we introduce GPOMDP, a simulation-based algorithm for generating a biased estimate of the gradient of the average reward in Partially Observable Markov Decision Processes (POMDPs) controlled by parameterized stochastic policies. A similar algorithm was proposed by Kimura, Yamamura, and Kobayashi (1995). The algorithm's chief advantages are that it requires storage of only twice the number of policy parameters, uses one free parameter β ∈ [0,1) (which has a natural interpretation in terms of bias-variance trade-off), and requires no knowledge of the underlying state. We prove convergence of GPOMDP, and show how the correct choice of the parameter β is related to the mixing time of the controlled POMDP. We briefly describe extensions of GPOMDP to controlled Markov chains, continuous state, observation and control spaces, multiple-agents, higher-order derivatives, and a version for training stochastic policies with internal states. In a companion paper (Baxter, Bartlett, & Weaver, 2001) we show how the gradient estimates generated by GPOMDP can be used in both a traditional stochastic gradient algorithm and a conjugate-gradient procedure to find local optima of the average reward. ©2001 AI Access Foundation and Morgan Kaufmann Publishers. All rights reserved.

Exponentiated gradient algorithms for conditional random fields and max-margin Markov Networks

Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of parameters in these models is therefore an important problem, and becomes a key factor when learning from very large data sets. This paper describes exponentiated gradient (EG) algorithms for training such models, where EG updates are applied to the convex dual of either the log-linear or max-margin objective function; the dual in both the log-linear and max-margin cases corresponds to minimizing a convex function with simplex constraints. We study both batch and online variants of the algorithm, and provide rates of convergence for both cases. In the max-margin case, O(1/ε) EG updates are required to reach a given accuracy ε in the dual; in contrast, for log-linear models only O(log(1/ε)) updates are required. For both the max-margin and log-linear cases, our bounds suggest that the online EG algorithm requires a factor of n less computation to reach a desired accuracy than the batch EG algorithm, where n is the number of training examples. Our experiments confirm that the online algorithms are much faster than the batch algorithms in practice. We describe how the EG updates factor in a convenient way for structured prediction problems, allowing the algorithms to be efficiently applied to problems such as sequence learning or natural language parsing. We perform extensive evaluation of the algorithms, comparing them to L-BFGS and stochastic gradient descent for log-linear models, and to SVM-Struct for max-margin models. The algorithms are applied to a multi-class problem as well as to a more complex large-scale parsing task. In all these settings, the EG algorithms presented here outperform the other methods.

Natural convection from a plane vertical porous surface in non-isothermal surroundings

In this paper, laminar natural convection flow from a permeable and isothermal vertical surface placed in non-isothermal surroundings is considered. Introducing appropriate transformations into the boundary layer equations governing the flow derives non-similar boundary layer equations. Results of both the analytical and numerical solutions are then presented in the form of skin-friction and Nusselt number. Numerical solutions of the transformed non-similar boundary layer equations are obtained by three distinct solution methods, (i) the perturbation solutions for small � (ii) the asymptotic solution for large � (iii) the implicit finite difference method for all � where � is the transpiration parameter. Perturbation solutions for small and large values of � are compared with the finite difference solutions for different values of pertinent parameters, namely, the Prandtl number Pr, and the ambient temperature gradient n.

Effect of MHD and heat generation on natural convection flow in an open square cavity under microgravity condition

Purpose - Thermo-magnetic convection and heat transfer of paramagnetic fluid placed in a micro-gravity condition (g = 0) and under a uniform vertical gradient magnetic field in an open square cavity with three cold sidewalls have been studied numerically. Design/methodology/approach - This magnetic force is proportional to the magnetic susceptibility and the gradient of the square of the magnetic induction. The magnetic susceptibility is inversely proportional to the absolute temperature based on Curie’s law. Thermal convection of a paramagnetic fluid can therefore take place even in zero-gravity environment as a direct consequence of temperature differences occurring within the fluid due to a constant internal heat generation placed within a magnetic field gradient. Findings - Effects of magnetic Rayleigh number, Ra, Prandtl number, Pr, and paramagnetic fluid parameter, m, on the flow pattern and isotherms as well as on the heat absorption are presented graphically. It is found that the heat transfer rate is suppressed in increased of the magnetic Rayleigh number and the paramagnetic fluid parameter for the present investigation. Originality/value - It is possible to control the buoyancy force by using the super conducting magnet. To the best knowledge of the author no literature related to magnetic convection for this configuration is available.

Prandtl number scaling of the unsteady natural convection boundary layer adjacent to a vertical flat plate for Pr > 1 subject to ramp surface heat flux

It is found in the literature that the existing scaling results for the boundary layer thickness, velocity and steady state time for the natural convection flow over an evenly heated plate provide a very poor prediction of the Prandtl number dependency of the flow. However, those scalings provide a good prediction of two other governing parameters’ dependency, the Rayleigh number and the aspect ratio. Therefore, an improved scaling analysis using a triple-layer integral approach and direct numerical simulations have been performed for the natural convection boundary layer along a semi-infinite flat plate with uniform surface heat flux. This heat flux is a ramp function of time, where the temperature gradient on the surface increases with time up to some specific time and then remains constant. The growth of the boundary layer strongly depends on the ramp time. If the ramp time is sufficiently long, the boundary layer reaches a quasi steady mode before the growth of the temperature gradient is completed. In this mode, the thermal boundary layer at first grows in thickness and then contracts with increasing time. However, if the ramp time is sufficiently short, the boundary layer develops differently, but after the wall temperature gradient growth is completed, the boundary layer develops as though the startup had been instantaneous.

A fuzzy dynamic flood routing model for natural channels

A fuzzy dynamic flood routing model (FDFRM) for natural channels is presented, wherein the flood wave can be approximated to a monoclinal wave. This study is based on modification of an earlier published work by the same authors, where the nature of the wave was of gravity type. Momentum equation of the dynamic wave model is replaced by a fuzzy rule based model, while retaining the continuity equation in its complete form. Hence, the FDFRM gets rid of the assumptions associated with the momentum equation. Also, it overcomes the necessity of calculating friction slope (S-f) in flood routing and hence the associated uncertainties are eliminated. The fuzzy rule based model is developed on an equation for wave velocity, which is obtained in terms of discontinuities in the gradient of flow parameters. The channel reach is divided into a number of approximately uniform sub-reaches. Training set required for development of the fuzzy rule based model for each sub-reach is obtained from discharge-area relationship at its mean section. For highly heterogeneous sub-reaches, optimized fuzzy rule based models are obtained by means of a neuro-fuzzy algorithm. For demonstration, the FDFRM is applied to flood routing problems in a fictitious channel with single uniform reach, in a fictitious channel with two uniform sub-reaches and also in a natural channel with a number of approximately uniform sub-reaches. It is observed that in cases of the fictitious channels, the FDFRM outputs match well with those of an implicit numerical model (INM), which solves the dynamic wave equations using an implicit numerical scheme. For the natural channel, the FDFRM Outputs are comparable to those of the HEC-RAS model.

Nitrous oxide emissions from selected natural and managed northern ecosystems

Microbial activity in soils is the main source of nitrous oxide (N2O) to the atmosphere. Nitrous oxide is a strong greenhouse gas in the troposphere and participates in ozone destructive reactions in the stratosphere. The constant increase in the atmospheric concentration, as well as uncertainties in the known sources and sinks of N2O underline the need to better understand the processes and pathways of N2O in terrestrial ecosystems. This study aimed at quantifying N2O emissions from soils in northern Europe and at investigating the processes and pathways of N2O from agricultural and forest ecosystems. Emissions were measured in forest ecosystems, agricultural soils and a landfill, using the soil gradient, chamber and eddy covariance methods. Processes responsible for N2O production, and the pathways of N2O from the soil to the atmosphere, were studied in the laboratory and in the field. These ecosystems were chosen for their potential importance to the national and global budget of N2O. Laboratory experiments with boreal agricultural soils revealed that N2O production increases drastically with soil moisture content, and that the contribution of the nitrification and denitrification processes to N2O emissions depends on soil type. Laboratory study with beech (Fagus sylvatica) seedlings demonstrated that trees can serve as conduits for N2O from the soil to the atmosphere. If this mechanism is important in forest ecosystems, the current emission estimates from forest soils may underestimate the total N2O emissions from forest ecosystems. Further field and laboratory studies are needed to evaluate the importance of this mechanism in forest ecosystems. The emissions of N2O from northern forest ecosystems and a municipal landfill were highly variable in time and space. The emissions of N2O from boreal upland forest soil were among the smallest reported in the world. Despite the low emission rates, the soil gradient method revealed a clear seasonal variation in N2O production. The organic topsoil was responsible for most of the N2O production and consumption in this forest soil. Emissions from the municipal landfill were one to two orders of magnitude higher than those from agricultural soils, which are the most important source of N2O to the atmosphere. Due to their small areal coverage, landfills only contribute minimally to national N2O emissions in Finland. The eddy covariance technique was demonstrated to be useful for measuring ecosystem-scale emissions of N2O in forest and landfill ecosystems. Overall, more measurements and integration between different measurement techniques are needed to capture the large variability in N2O emissions from natural and managed northern ecosystems.

Characterization of groundwater chemistry under the influence of lithologic and anthropogenic factors along a climatic gradient in Upper Cauvery basin, South India

Hydrogeological and climatic effect on chemical behavior of groundwater along a climatic gradient is studied along a river basin. `Semi-arid' (500-800 mm of mean annual rainfall), `sub-humid' (800-1,200 mm/year) and `humid' (1,200-1,500 mm/year) are the climatic zones chosen along the granito-gneissic plains of Kabini basin in South India for the present analysis. Data on groundwater chemistry is initially checked for its quality using NICB ratio (<+/- 5 %), EC versus TZ+ (similar to 0.85 correlation), EC versus TDS and EC versus TH analysis. Groundwater in the three climatic zones is `hard' to `very hard' in terms of Ca-Mg hardness. Polluted wells are identified (> 40 % of pollution) and eliminated for the characterization. Piper's diagram with mean concentrations indicates the evolution of CaNaHCO3 (semi-arid) from CaHCO3 (humid zone) along the climatic gradient. Carbonates dominate other anions and strong acids exceeded weak acids in the region. Mule Hole SEW, an experimental watershed in sub-humid zone, is characterized initially using hydrogeochemistry and is observed to be a replica of entire sub-humid zone (with 25 wells). Extension of the studies for the entire basin (120 wells) showed a chemical gradient along the climatic gradient with sub-humid zone bridging semi-arid and humid zones. Ca/Na molar ratio varies by more than 100 times from semi-arid to humid zones. Semi-arid zone is more silicaceous than sub-humid while humid zone is more carbonaceous (Ca/Cl similar to 14). Along the climatic gradient, groundwater is undersaturated (humid), saturated (sub-humid) and slightly supersaturated (semi-arid) with calcite and dolomite. Concentration-depth profiles are in support of the geological stratification i.e., not approximate to 18 m of saprolite and similar to 25 m of fracture rock with parent gneiss beneath. All the wells are classified into four groups based on groundwater fluctuations and further into `deep' and `shallow' based on the depth to groundwater. Higher the fluctuations, larger is its impact on groundwater chemistry. Actual seasonal patterns are identified using `recharge-discharge' concept based on rainfall intensity instead of traditional monsoon-non-monsoon concept. Non-pumped wells have low Na/Cl and Ca/Cl ratios in recharge period than in discharge period (Dilution). Few other wells, which are subjected to pumping, still exhibit dilution chemistry though water level fluctuations are high due to annual recharge. Other wells which do not receive sufficient rainfall and are constantly pumped showed high concentrations in recharge period rather than in discharge period (Anti-dilution). In summary, recharge-discharge concept demarcates the pumped wells from natural deep wells thus, characterizing the basin.

Avaliação de Correlações e Equações de Estado para Determinação de Fatores de Compressibilidade de Gás Natural

O fator de compressibilidade (Z) de gás natural é utilizado em vários cálculos na engenharia de petróleo (avaliação de formações, perda de carga em tubulações, gradiente de pressão em poços de gás, cálculos de balanço de massa, medição de gás, compressão e processamento de gás). As fontes mais comuns de valores de Z são medições experimentais, caras e demoradas. Essa propriedade também é estimada por correlações empíricas, modelos baseados no princípio dos estados correspondentes ou equações de estado (EOS). Foram avaliadas as capacidades das EOS de Soave-Redlich-Kwong (SRK), Peng-Robinson (PR), Patel-Teja (PT), Patel-Teja-Valderrama (PTV), Schmidt-Wenzel (SW), Lawal-Lake-Silberberg (LLS) e AGA-8 para previsão desta propriedade em aproximadamente 2200 pontos de dados experimentais. Estes pontos foram divididos em quatro grupos: Grupo 1 (Presença de frações C7+, Grupo 2 (temperaturas inferiores a 258,15 K), Grupo 3 (pressões superiores a 10000 kPa) e Grupo 4 (pressões inferiores a 10000 kPa). Os cálculos utilizando as equações de estado sob diferentes esquemas de previsão de coeficientes binários de interação foram cuidadosamente investigados. Os resultados sugerem que a EOS AGA-8 apresenta os menores erros para pressões de até 70000 kPa. Entretanto, observou-se uma tendência de aumento nos desvios médios absolutos em função das concentrações de CO2 e H2S. As EOS PTV e a EOS SW são capazes de predizer o fator de compressibilidade (Z) com desvios médios absolutos entre os valores calculados e experimentais com precisão satisfatória para a maioria das aplicações, para uma variada faixa de temperatura e pressão. Este estudo também apresenta uma avaliação de 224 métodos de cálculo de Z onde foram utilizadas 8 correlações combinadas com 4 regras de mistura para estimativa de temperaturas e pressões pseudorreduzidas das amostras, junto com 7 métodos de caracterização das propriedades críticas da fração C7+, quando presente na composição do gás. Em função dos resultados são sugeridas, para diferentes tipos de sistemas, as melhores combinações de correlações com regras de mistura capazes de predizer fatores de compressibilidade (Z) com os menores erros absolutos médios relativos

Code-specific policy gradient rules for spiking neurons

Although it is widely believed that reinforcement learning is a suitable tool for describing behavioral learning, the mechanisms by which it can be implemented in networks of spiking neurons are not fully understood. Here, we show that different learning rules emerge from a policy gradient approach depending on which features of the spike trains are assumed to influence the reward signals, i.e., depending on which neural code is in effect. We use the framework of Williams (1992) to derive learning rules for arbitrary neural codes. For illustration, we present policy-gradient rules for three different example codes - a spike count code, a spike timing code and the most general "full spike train" code - and test them on simple model problems. In addition to classical synaptic learning, we derive learning rules for intrinsic parameters that control the excitability of the neuron. The spike count learning rule has structural similarities with established Bienenstock-Cooper-Munro rules. If the distribution of the relevant spike train features belongs to the natural exponential family, the learning rules have a characteristic shape that raises interesting prediction problems.

Annealing study of carrier concentration in gradient-doped GaAs/GaAlAs epilayers grown by molecular beam epitaxy

We measured the carrier concentration distribution of gradient-doped GaAs/GqAlAs epilayers grown by molecular beam epitaxy before and after annealing at 600 degrees C, using electrochemical capacitance voltage profiling, to investigate the internal variation of transmission-mode GaAs photocathodes arising from the annealing process. The results show that the carrier concentration increased after annealing. As a result, the total band-bending energy in the gradient-doped GaAs emission layer increased by 25.24% after annealing, which improves the pbotoexcited electron movement toward the surface. On the other hand, the annealing process resulted in a worse carrier concentration discrepancy between the GaAs and the GaAlAs, which causes a lower back interface potential barrier, decreasing the amount of high-energy photoelectrons. (C) 2009 Optical Society of America

Investigation of Interfacial Tensions for Carbon Dioxide Aqueous Solutions by Perturbed-Chain Statistical Associating Fluid Theory Combined with Density-Gradient Theory

The perturbed-chain statistical associating fluid theory and density-gradient theory are used to construct an equation of state (EOS) applicable for the phase behaviors of carbon dioxide aqueous solutions. With the molecular parameters and influence parameters respectively regressed from bulk properties and surface tensions of pure fluids as input, both the bulk and interfacial properties of carbon dioxide aqueous solutions are satisfactorily correlated by adjusting the binary interaction parameter (k(ij)). Our results show that the constructed EOS is able to describe the interfacial properties of carbon dioxide aqueous solutions in a wide temperature range, and illustrate the influences of temperature, pressure, and densities in each phase on the interfacial properties.

CH abundance gradient in TMC-1

Aims. The aim of this study is to examine if the well-known chemical gradient in TMC-1 is reflected in the amount of rudimentary forms of carbon available in the gas-phase. As a tracer we use the CH radical which is supposed to be well correlated with carbon atoms and simple hydrocarbon ions. Methods. We observed the 9-cm ?-doubling lines of CH along the dense filament of TMC-1. The CH column densities were compared with the total H2 column densities derived using the 2MASS NIR data and previously published SCUBA maps and with OH column densities derived using previous observations with Effelsberg. We also modelled the chemical evolution of TMC-1 adopting physical conditions typical of dark clouds using the UMIST Database for Astrochemistry gas-phase reaction network to aid the interpretation of the observed OH/CH abundance ratios. Results. The CH column density has a clear peak in the vicinity of the cyanopolyyne maximum of TMC-1. The fractional CH abundance relative to H2 increases steadily from the northwestern end of the filament where it lies around 1.0 × 10-8 , to the southeast where it reaches a value of 2.0 × 10-8. The OH and CH column densities are well correlated, and we obtained OH/CH abundance ratios of ~16–20. These values are clearly larger than what has been measured recently in diffuse interstellar gas and is likely to be related to C to CO conversion at higher densities. The good correlation between CH and OH can be explained by similar production and destruction pathways. We suggest that the observed CH and OH abundance gradients are mainly due to enhanced abundances in a low-density envelope which becomes more prominent in the southeastern part and seems to continue beyond the dense filament. Conclusions. An extensive envelope probably signifies an early stage of dynamical evolution, and conforms with the detection of a large CH abundance in the southeastern part of the cloud. The implied presence of other simple forms of carbon in the gas phase provides a natural explanation for the observation of “early-type” molecules in this region.

Design of neuro-fuzzy models by evolutionary and gradient-based algorithms

All systems found in nature exhibit, with different degrees, a nonlinear behavior. To emulate this behavior, classical systems identification techniques use, typically, linear models, for mathematical simplicity. Models inspired by biological principles (artificial neural networks) and linguistically motivated (fuzzy systems), due to their universal approximation property, are becoming alternatives to classical mathematical models. In systems identification, the design of this type of models is an iterative process, requiring, among other steps, the need to identify the model structure, as well as the estimation of the model parameters. This thesis addresses the applicability of gradient-basis algorithms for the parameter estimation phase, and the use of evolutionary algorithms for model structure selection, for the design of neuro-fuzzy systems, i.e., models that offer the transparency property found in fuzzy systems, but use, for their design, algorithms introduced in the context of neural networks. A new methodology, based on the minimization of the integral of the error, and exploiting the parameter separability property typically found in neuro-fuzzy systems, is proposed for parameter estimation. A recent evolutionary technique (bacterial algorithms), based on the natural phenomenon of microbial evolution, is combined with genetic programming, and the resulting algorithm, bacterial programming, advocated for structure determination. Different versions of this evolutionary technique are combined with gradient-based algorithms, solving problems found in fuzzy and neuro-fuzzy design, namely incorporation of a-priori knowledge, gradient algorithms initialization and model complexity reduction.