Disadvantages of traditional reinforcement learning techniques are complicated structures and that training algorithms are often reliant on the derivative information of the problem domain and also require a priori information of the network architecture. Such handicaps are overcome in this paper with the use of ‘messy genetic algorithms’, whose main characteristic is a variable length chromoso...

Empirical modelling in high dimensional spaces is usually preceded by a feature selection stage. Irrelevant or noisy features unnecessarily increase the complexity of the problem and can degrade modelling performance. Here, multiobjective genetic algorithms are proposed as effective means of evolving a diverse population of alternative feature sets with various accuracy/complexity trade-offs. T...

A Fuzzy logic system has been shown to be able to arbitrarily approximate any nonlinear function and has been successfully applied to system modelling. The functional rule fuzzy system enables the input-output relation of the fuzzy logic system to be analysed. B-spline basis functions have many desirable numerical properties and as such can be used as membership functions of fuzzy system. This ...

Neurofuzzy systems have been widely applied to a diverse range of applications because their robust operation and network transparency. A neurofuzzy system is specified by a set of rules with confidences. However, as knowledge base systems, neurofuzzy systems suffer from the curse of dimensionality i.e., exponential increase in the demand of resources and in the number of rules. So, the interpr...

Credit-risk evaluation decisions are important for the ®nancial institutions involved due to the high level of risk associated with wrong decisions. The process of making credit-risk evaluation decision is complex and unstructured. Neural networks are known to perform reasonably well compared to alternate methods for this problem. However, a drawback of using neural networks for credit-risk eva...

A neurofuzzy methodology is presented for motion planning in semi-autonomous mobile robots. The robotic automata considered are devices whose main feature is incremental learning from a human instructor. Fuzzy descriptions are used for the robot to acquire a repertoire of behaviors from an instructor which it may subsequently refine and recall using neural adaptive techniques. The robot is endo...