Fuzzy Coding of Genetic Algorithms

A well-cited paper suggesting fuzzy coding as an alternative to the conventional binary, grey and floating-point representations used in genetic algorithms.

The Galactic metallicity gradient

We have previously published intermediate to hi,oh resolution spectroscopic observations of approximately 80 early B-type main-sequence stars situated in 19 Galactic open clusters/associations with Galactocentric distances distributed over 6 less than or equal to R-g less than or equal to 18 kpc. This current study collates and re-analyses these equivalent- width datasets using LTE and non-LTE model atmosphere techniques, in order to determine the stellar atmospheric parameters and abundance estimates for C, N, O, Mg, Al and Si. The latter should be representative of the present-day Galactic interstellar medium. Our extensive observational dataset permits the identification of sub-samples of stars with similar atmospheric parameters and of homogeneous subsets of lines. As such, this investigation represents the most extensive and systematic study of its kind to date. We conclude that the distribution of light elements (CI O, Mg & Si) in the Galactic disk can be represented by a linear, radial gradient of -0.07 +/- 0.01 dex kpc(-1) Our results for nitrogen and oxygen viz. (-0.09 +/- 0.01 dex kpc(-1) and -0.067 +/- 0.008 dex kpc(-1)) are in excellent agreement with that found from the study of HII regions. We have also examined our datasets for evidence of an abrupt discontinuity in the metallicity of the Galactic disk near a Galactocentric distance of 10 kpc (see Twarog et al. 1997). However, there is no evidence to suggest that our data would be better fitted with a two-zone model. Moreover, we observe a N/O gradient of -0.04 +/- 0.02 dex kpc(-1) which is consistent with that found for other spiral galaxies (Vila- Costas gr Edmunds 1993).

Difference equation sensor characterization algorithms for two-thermocouple probes

The characterization of thermocouple sensors for temperature measurement in variable flow environments is a challenging problem. In this paper, novel difference equation-based algorithms are presented that allow in situ characterization of temperature measurement probes consisting of two-thermocouple sensors with differing time constants. Linear and non-linear least squares formulations of the characterization problem are introduced and compared in terms of their computational complexity, robustness to noise and statistical properties. With the aid of this analysis, least squares optimization procedures that yield unbiased estimates are identified. The main contribution of the paper is the development of a linear two-parameter generalized total least squares formulation of the sensor characterization problem. Monte-Carlo simulation results are used to support the analysis.

Computationally efficient sequential learning algorithms for direct link resource-allocating networks

Computionally efficient sequential learning algorithms are developed for direct-link resource-allocating networks (DRANs). These are achieved by decomposing existing recursive training algorithms on a layer by layer and neuron by neuron basis. This allows network weights to be updated in an efficient parallel manner and facilitates the implementation of minimal update extensions that yield a significant reduction in computation load per iteration compared to existing sequential learning methods employed in resource-allocation network (RAN) and minimal RAN (MRAN) approaches. The new algorithms, which also incorporate a pruning strategy to control network growth, are evaluated on three different system identification benchmark problems and shown to outperform existing methods both in terms of training error convergence and computational efficiency. (c) 2005 Elsevier B.V. All rights reserved.

Genetic algorithms for local controller network construction

Local Controller Networks (LCNs) provide nonlinear control by interpolating between a set of locally valid, subcontrollers covering the operating range of the plant. Constructing such networks typically requires knowledge of valid local models. This paper describes a new genetic learning approach to the construction of LCNs directly from the dynamic equations of the plant, or from modelling data. The advantage is that a priori knowledge about valid local models is not needed. In addition to allowing simultaneous optimisation of both the controller and validation function parameters, the approach aids transparency by ensuring that each local controller acts independently of the rest at its operating point. It thus is valuable for simultaneous design of the LCNs and identification of the operating regimes of an unknown plant. Application results from a highly nonlinear pH neutralisation process and its associated neural network representation are utilised to illustrate these issues.

Comparison of two construction algorithms for local model networks

The divide-and-conquer approach of local model (LM) networks is a common engineering approach to the identification of a complex nonlinear dynamical system. The global representation is obtained from the weighted sum of locally valid, simpler sub-models defined over small regions of the operating space. Constructing such networks requires the determination of appropriate partitioning and the parameters of the LMs. This paper focuses on the structural aspect of LM networks. It compares the computational requirements and performances of the Johansen and Foss (J&F) and LOLIMOT tree-construction algorithms. Several useful and important modifications to each algorithm are proposed. The modelling performances are evaluated using real data from a pilot plant of a pH neutralization process. Results show that while J&F achieves a more accurate nonlinear representation of the pH process, LOLIMOT requires significantly less computational effort.