Soft Computing and Intelligent Systems: Theory and Applications
The field of soft computing is emerging from the cutting edge research over the last ten years devoted to fuzzy engineering and genetic algorithms. The subject is being called soft computing and computational intelligence. With acceptance of the research fundamentals in these important areas, the field is expanding into direct applications through engineering and systems science.
This book cover the fundamentals of this emerging filed, as well as direct applications and case studies. There is a need for practicing engineers, computer scientists, and system scientists to directly apply "fuzzy" engineering into a wide array of devices and systems.
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adaptive control agents applications approach approximation architecture array Artificial Intelligence back-propagation basis functions behavior cargo ship chromosome classification cognitive complex Conf connected constraint convergence data flow defined Engineering equation estimation example expert system feedforward fuzzy logic fuzzy model fuzzy neural networks fuzzy numbers fuzzy rules fuzzy sets Fuzzy Systems genetic algorithms hidden layer hybrid systems identification IEEE IEEE Trans image fusion implementation input intelligent control Intelligent Control Systems Intelligent Systems iterative learning control knowledge knowledge-based learning control linear M.M. Gupta machine mathematical matrix membership functions methods neurofuzzy neurons nodes nonlinear systems operation optimal output parallel Parallel Computing parameters pattern perceptron performance Petri nets plant population predictive control problem Proc processor programming pyramid real-time represent representation robot sensor shown in Figure signal simulation soft computing solution solving strategy supervised learning synaptic techniques theory trajectory values variable vector weights