Evolutionary modular neural networks for intelligent systems

Evolutionary Design of Intelligent Systems in Modeling, Simulation and Control

The editors describe in this book, new methods for evolutionary design of intelligent systems using soft computing and their applications in modeling, simulation and control. Soft Computing (SC) consists of several intelligent computing paradigms, including fuzzy logic, neural networks, and evolutionary algorithms, which can be used to produce powerful hybrid intelligent systems. The book is or...

Modeling and Evolutionary Learning of Modular Neural Networks

Modular Neural Networks Qiangfu Zhao The University of Aizu Aizu-Wakamatsu, Fukushima 965-8580, Japan Email: [email protected] Abstract In the last decade, a number of neural network models have been proposed in the literature. Some of them have been successfully incorporated in di erent intelligent information processing systems. Among these models, a group of most successful ones are the m...

Distribution Systems Reconfiguration Using Pattern Recognizer Neural Networks

A novel intelligent neural optimizer with two objective functions is designed for electrical distribution systems. The presented method is faster than alternative optimization methods and is comparable with the most powerful and precise ones. This optimizer is much smaller than similar neural systems. In this work, two intelligent estimators are designed, a load flow program is coded, and a spe...

Signature Recognition with a Hybrid Approach Combining Modular Neural Networks and Fuzzy Logic for Response Integration

Evolution of Modular Neural Network in Medical Diagnosis

Intelligent systems have been extensively used in the area of biomedical engineering for assisting the doctors in medical diagnosis. The inability of simple neural networks to solve the diagnosis problem, due to extensively large data size as well as complex mapping of inputs to outputs, has resulted in the growth of modular neural networks that try to exploit the modularity in the problem for ...