A neural network enhanced generalized minimum variance self-tuning proportional, integral and derivative control algorithm for complex dynamic systems

A neural network enhanced proportional, integral and derivative (PID) controller is presented that combines the attributes of neural network learning with a generalized minimum-variance self-tuning control (STC) strategy. The neuro PID controller is structured with plant model identification and PID parameter tuning. The plants to be controlled are approximated by an equivalent model composed of a simple linear submodel to approximate plant dynamics around operating points, plus an error agent to accommodate the errors induced by linear submodel inaccuracy due to non-linearities and other complexities. A generalized recursive least-squares algorithm is used to identify the linear submodel, and a layered neural network is used to detect the error agent in which the weights are updated on the basis of the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model, and therefore the error agent is naturally functioned within the control law. In this way the controller can deal not only with a wide range of linear dynamic plants but also with those complex plants characterized by severe non-linearity, uncertainties and non-minimum phase behaviours. Two simulation studies are provided to demonstrate the effectiveness of the controller design procedure.

Self-tuning proportional, integral and derivative controller based on genetic algorithm least squares

A self-tuning proportional, integral and derivative control scheme based on genetic algorithms (GAs) is proposed and applied to the control of a real industrial plant. This paper explores the improvement in the parameter estimator, which is an essential part of an adaptive controller, through the hybridization of recursive least-squares algorithms by making use of GAs and the possibility of the application of GAs to the control of industrial processes. Both the simulation results and the experiments on a real plant show that the proposed scheme can be applied effectively.

Mean-tracking clustering algorithm for radial basis function centre selection

Radial basis functions can be combined into a network structure that has several advantages over conventional neural network solutions. However, to operate effectively the number and positions of the basis function centres must be carefully selected. Although no rigorous algorithm exists for this purpose, several heuristic methods have been suggested. In this paper a new method is proposed in which radial basis function centres are selected by the mean-tracking clustering algorithm. The mean-tracking algorithm is compared with k means clustering and it is shown that it achieves significantly better results in terms of radial basis function performance. As well as being computationally simpler, the mean-tracking algorithm in general selects better centre positions, thus providing the radial basis functions with better modelling accuracy

Predictive algorithm for the roll control autopilot of a jet fighter aircraft

Predictive controllers are often only applicable for open-loop stable systems. In this paper two such controllers are designed to operate on open-loop critically stable systems, each of which is used to find the control inputs for the roll control autopilot of a jet fighter aircraft. It is shown how it is quite possible for good predictive control to be achieved on open-loop critically stable systems.

A mixture of experts network structure construction algorithm for modelling and control

This paper introduces a new fast, effective and practical model structure construction algorithm for a mixture of experts network system utilising only process data. The algorithm is based on a novel forward constrained regression procedure. Given a full set of the experts as potential model bases, the structure construction algorithm, formed on the forward constrained regression procedure, selects the most significant model base one by one so as to minimise the overall system approximation error at each iteration, while the gate parameters in the mixture of experts network system are accordingly adjusted so as to satisfy the convex constraints required in the derivation of the forward constrained regression procedure. The procedure continues until a proper system model is constructed that utilises some or all of the experts. A pruning algorithm of the consequent mixture of experts network system is also derived to generate an overall parsimonious construction algorithm. Numerical examples are provided to demonstrate the effectiveness of the new algorithms. The mixture of experts network framework can be applied to a wide variety of applications ranging from multiple model controller synthesis to multi-sensor data fusion.

Variable selection algorithm for the construction of MIMO operating point dependent neurofuzzy networks

An input variable selection procedure is introduced for the identification and construction of multi-input multi-output (MIMO) neurofuzzy operating point dependent models. The algorithm is an extension of a forward modified Gram-Schmidt orthogonal least squares procedure for a linear model structure which is modified to accommodate nonlinear system modeling by incorporating piecewise locally linear model fitting. The proposed input nodes selection procedure effectively tackles the problem of the curse of dimensionality associated with lattice-based modeling algorithms such as radial basis function neurofuzzy networks, enabling the resulting neurofuzzy operating point dependent model to be widely applied in control and estimation. Some numerical examples are given to demonstrate the effectiveness of the proposed construction algorithm.

Givens rotation based fast backward elimination algorithm for RBF neural network pruning

A fast backward elimination algorithm is introduced based on a QR decomposition and Givens transformations to prune radial-basis-function networks. Nodes are sequentially removed using an increment of error variance criterion. The procedure is terminated by using a prediction risk criterion so as to obtain a model structure with good generalisation properties. The algorithm can be used to postprocess radial basis centres selected using a k-means routine and, in this mode, it provides a hybrid supervised centre selection approach.

Prebiotic evaluation of cocoa-derived flavanols in healthy humans by using a randomized, controlled, double-blind, crossover intervention study

BACKGROUND: The absorption of cocoa flavanols in the small intestine is limited, and the majority of the flavanols reach the large intestine where they may be metabolized by resident microbiota. OBJECTIVE: We assessed the prebiotic potential of cocoa flavanols in a randomized, double-blind, crossover, controlled intervention study. DESIGN: Twenty-two healthy human volunteers were randomly assigned to either a high-cocoa flavanol (HCF) group (494 mg cocoa flavanols/d) or a low-cocoa flavanol (LCF) group (23 mg cocoa flavanols/d) for 4 wk. This was followed by a 4-wk washout period before volunteers crossed to the alternant arm. Fecal samples were recovered before and after each intervention, and bacterial numbers were measured by fluorescence in situ hybridization. A number of other biochemical and physiologic markers were measured. RESULTS: Compared with the consumption of the LCF drink, the daily consumption of the HCF drink for 4 wk significantly increased the bifidobacterial (P < 0.01) and lactobacilli (P < 0.001) populations but significantly decreased clostridia counts (P < 0.001). These microbial changes were paralleled by significant reductions in plasma triacylglycerol (P < 0.05) and C-reactive protein (P < 0.05) concentrations. Furthermore, changes in C-reactive protein concentrations were linked to changes in lactobacilli counts (P < 0.05, R(2) = -0.33 for the model). These in vivo changes were closely paralleled by cocoa flavanol-induced bacterial changes in mixed-batch culture experiments. CONCLUSION: This study shows, for the first time to our knowledge, that consumption of cocoa flavanols can significantly affect the growth of select gut microflora in humans, which suggests the potential prebiotic benefits associated with the dietary inclusion of flavanol-rich foods. This trial was registered at clinicaltrials.gov as NCT01091922.

Blind equalization of nonminimum phase channels: higher order cumulant based algorithm

Higher order cumulant analysis is applied to the blind equalization of linear time-invariant (LTI) nonminimum-phase channels. The channel model is moving-average based. To identify the moving average parameters of channels, a higher-order cumulant fitting approach is adopted in which a novel relay algorithm is proposed to obtain the global solution. In addition, the technique incorporates model order determination. The transmitted data are considered as independently identically distributed random variables over some discrete finite set (e.g., set {±1, ±3}). A transformation scheme is suggested so that third-order cumulant analysis can be applied to this type of data. Simulation examples verify the feasibility and potential of the algorithm. Performance is compared with that of the noncumulant-based Sato scheme in terms of the steady state MSE and convergence rate.

Robust blind deconvolution algorithm: variance approximation and series decoupling

An alternative blind deconvolution algorithm for white-noise driven minimum phase systems is presented and verified by computer simulation. This algorithm uses a cost function based on a novel idea: variance approximation and series decoupling (VASD), and suggests that not all autocorrelation function values are necessary to implement blind deconvolution.

Simple adaptive momentum: new algorithm for training multilayer perceptrons

The speed of convergence while training is an important consideration in the use of neural nets. The authors outline a new training algorithm which reduces both the number of iterations and training time required for convergence of multilayer perceptrons, compared to standard back-propagation and conjugate gradient descent algorithms.

Cholesterol reduction using manufactured foods high in monounsaturated fatty acids: a randomized crossover study

In two separate studies, the cholesterol-lowering efficacy of a diet high in monounsaturated fatty acids (MUFA) was evaluated by means of a randomized crossover trial. In both studies subjects were randomized to receive either a high-MUFA diet or the control diet first, which they followed for a period of 8 weeks; following a washout period of 4–6 weeks they were transferred onto the opposing diet for a further period of 8 weeks. In one study subjects were healthy middle-aged men (n 30), and in the other they were young men (n 23) with a family history of CHD recruited from two centres (Guildford and Dublin). The two studies were conducted over the same time period using identical foods and study designs. Subjects consumed 38% energy as fat, with 18% energy as MUFA and 10% as saturated fatty acids (MUFA diet), or 13% energy as MUFA and 16% as saturated fatty acids (control diet). The polyunsaturated fatty acid content of each diet was 7%. The diets were achieved by providing subjects with manufactured foods such as spreads, ‘ready meals’, biscuits, puddings and breads, which, apart from their fatty acid compositions, were identical for both diets. Subjects were blind to which of the diets they were following on both arms of the study. Weight changes on the diets were less than 1 kg. In the groups combined (n 53) mean total and LDL-cholesterol levels were significantly lower at the end of the MUFA diet than the control diet by 0×29 (SD 0×61) mmol/l (P,0×001) and 0×38 (SD 0×64) mmol/l (P, 0×0001) respectively. In middle-aged men these differences were due to a mean reduction in LDL-cholesterol of ¹11 (SD 12) % on the MUFA diet with no change on the control diet (¹1×1 (SD 10) %). In young men the differences were due to an increase in LDL-cholesterol concentration on the control diet of þ6×2 (SD 13) % and a decrease on the MUFA diet of ¹7×8 (SD 20) %. Differences in the responses of middle-aged and young men to the two diets did not appear to be due to differences in their habitual baseline diets which were generally similar, but appeared to reflect the lower baseline cholesterol concentrations in the younger men. There was a moderately strong and statistically significant inverse correlation between the change in LDLcholesterol concentration on each diet and the baseline fasting LDL-cholesterol concentration (r¹0×49; P,0×0005). In conclusion, diets in which saturated fat is partially replaced by MUFA can achieve significant reductions in total and LDL-cholesterol concentrations, even when total fat and energy intakes are maintained. The dietary approach used to alter fatty acid intakes would be appropriate for achieving reductions in saturated fat intakes in whole populations.

An LMS style variable tap-length algorithm for structure adaptation

Searching for the optimum tap-length that best balances the complexity and steady-state performance of an adaptive filter has attracted attention recently. Among existing algorithms that can be found in the literature, two of which, namely the segmented filter (SF) and gradient descent (GD) algorithms, are of particular interest as they can search for the optimum tap-length quickly. In this paper, at first, we carefully compare the SF and GD algorithms and show that the two algorithms are equivalent in performance under some constraints, but each has advantages/disadvantages relative to the other. Then, we propose an improved variable tap-length algorithm using the concept of the pseudo fractional tap-length (FT). Updating the tap-length with instantaneous errors in a style similar to that used in the stochastic gradient [or least mean squares (LMS)] algorithm, the proposed FT algorithm not only retains the advantages from both the SF and the GD algorithms but also has significantly less complexity than existing algorithms. Both performance analysis and numerical simulations are given to verify the new proposed algorithm.

Application of a novel optimal control algorithm to a benchmark fed-batch fermentation process

A novel algorithm for solving nonlinear discrete time optimal control problems with model-reality differences is presented. The technique uses Dynamic Integrated System Optimisation and Parameter Estimation (DISOPE) which has been designed to achieve the correct optimal solution in spite of deficiencies in the mathematical model employed in the optimisation procedure. A method based on Broyden's ideas is used for approximating some derivative trajectories required. Ways for handling con straints on both manipulated and state variables are described. Further, a method for coping with batch-to- batch dynamic variations in the process, which are common in practice, is introduced. It is shown that the iterative procedure associated with the algorithm naturally suits applications to batch processes. The algorithm is success fully applied to a benchmark problem consisting of the input profile optimisation of a fed-batch fermentation process.