I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

This paper presents a new evolutionary system, i.e., EPNet, for evolving artificial neural networks (ANNs). The evolutionary algorithm used in EPNet is based on Fogel's evolutionary programming (EP). Unlike most previous studies on evolving ANN's, this paper puts its emphasis on evolving ANN's behaviors. Five mutation operators proposed in EPNet reflect such an emphasis on evolving behaviors. Close behavioral links between parents and their offspring are maintained by various mutations, such as partial training and node splitting. EPNet evolves ANN's architectures and connection weights (including biases) simultaneously in order to reduce the noise in fitness evaluation. The parsimony of evolved ANN's is encouraged by preferring node/connection deletion to addition. EPNet has been tested on a number of benchmark problems in machine learning and ANNs, such as the parity problem, the medical diagnosis problems, the Australian credit card assessment problem, and the Mackey-Glass time series prediction problem. The experimental results show that EPNet can produce very compact ANNs with good generalization ability in comparison with other algorithms.

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A new evolutionary system for evolving artificial neural networks

Presents a constructive algorithm for training cooperative neural-network ensembles (CNNEs). CNNE combines ensemble architecture design with cooperative training for individual neural networks (NNs) in ensembles. Unlike most previous studies on training ensembles, CNNE puts emphasis on both accuracy and diversity among individual NNs in an ensemble. In order to maintain accuracy among individual NNs, the number of hidden nodes in individual NNs are also determined by a constructive approach. Incremental training based on negative correlation is used in CNNE to train individual NNs for different numbers of training epochs. The use of negative correlation learning and different training epochs for training individual NNs reflect CNNEs emphasis on diversity among individual NNs in an ensemble. CNNE has been tested extensively on a number of benchmark problems in machine learning and neural networks, including Australian credit card assessment, breast cancer, diabetes, glass, heart disease, letter recognition, soybean, and Mackey-Glass time series prediction problems. The experimental results show that CNNE can produce NN ensembles with good generalization ability.

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A constructive algorithm for training cooperative neural network ensembles

This paper is concerned with the simultaneous evolution of artificial neural network (ANN) architectures and weights. The current practice in evolving ANN's is to choose the best ANN in the last generation as the final result. This paper proposes a different approach to form the final result by combining all the individuals in the last generation in order to make best use of all the information contained in the whole population. This approach regards a population of ANN's as an ensemble and uses a combination method to integrate them. Although there has been some work on integrating ANN modules, little has been done in evolutionary learning to make best use of its population information. Four linear combination methods have been investigated in this paper to illustrate our ideas. Three real-world data sets have been used in our experimental studies, which show that the recursive least-square (RLS) algorithm always produces an integrated system that outperforms the best individual. The results confirm that a population contains more information than a single individual. Evolutionary learning should exploit such information to improve generalization of learned systems.

/pdf/making-use-of-population-information-in-evolutionary-2zczomug3g.pdf

Making use of population information in evolutionary artificial neural networks

In a radial basis function (RBF) network, the RBF centers and widths can be evolved by a cooperative-competitive genetic algorithm The set of genetic strings in one generation of the algorithm represents one REP network, not a population of competing networks This leads to moderate computation times for the algorithm as a whole Selection operates on individual RBFs rather than on whole networks Selection therefore requires a genetic fitness function that promotes competition among RBFs which are doing nearly the same job while at the same time promoting cooperation among RBFs which cover different parts of the domain of the function to be approximated Niche creation resulting from a fitness function of the form |w/sub i/|/sup /spl beta///E(|w/sub i'/|/sup /spl beta//), 1</spl beta/<2 can facilitate the desired cooperative-competitive behavior The feasibility of the resulting algorithm to evolve networks of Gaussian, inverse multiquadric, and thin-plate spline RBFs is demonstrated by predicting the Mackey-Glass time series For each type of RBF, and for networks of 25, 50, 75, 100, 125, and 150 RBF units, prediction errors for the evolved Gaussian RBF networks are 50-70% lower than RBF networks obtained by k-means clustering

Cooperative-competitive genetic evolution of radial basis function centers and widths for time series prediction

A new method to simultaneously estimate the radius of a cylindrical object and the wave propagation velocity from GPR data

Original Contribution: Evolutional development of a multilevel neural network

About simple fuzzy control and fuzzy control based on fuzzy relational equations

In this paper a new, generalized PSO (GPSO) algorithm is presented and analyzed, both theoretically and empirically. The new optimizer enables direct control over the properties of the search process. In addition, PSO is addressed in conceptually different manner, revealing further aspects of the algorithm behavior. GPSO is applied for training radial basis function neural network (RBF-NN) to identify dynamics of a nonlinear system. The target system is chosen to be of Lorenz type, known for its complex, chaotic behavior. Results presented in this paper clearly demonstrate effectiveness of the proposed algorithm.

Generalized PSO algorithm — an application to Lorenz system identification by means of neural-networks

This paper treats the problem of optimal control of a class of linear distributed parameter systems. Fourier series are used for transformation of a one-dimensional heat conduction equation into a system of ordinary differential equations. Necessary conditions for the optimal control are derived by means of Pontryagin's maximum principle. The application of obtained results is shown through two examples. The method in this paper is limited to linear systems.

Dušan Petrovački

Papers

A new method to simultaneously estimate the radius of a cylindrical object and the wave propagation velocity from GPR data

Original Contribution: Evolutional development of a multilevel neural network

About simple fuzzy control and fuzzy control based on fuzzy relational equations

Generalized PSO algorithm — an application to Lorenz system identification by means of neural-networks

Optimal control of a heat conduction problem