Computational Intelligence for Agent-based Systems

Neural Networks: Computational Models and Applications presents important theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational intelligence: pattern recognition, uniform approximation, constrained optimization, NP-hard problems, and image segmentation. The book offers a compact, insightful understanding of the broad and rapidly growing neural networks domain.

neural Networks: Computational Models And Applications Covers A Wealth Of Important Theoretical And Practical Issues In Neural Networks, Including The Learning Algorithms Of Feed-forward Neural Networks, Various Dynamical Properties Of Recurrent Neural Networks, Winner-take-all Networks And Their Applications In Broad Manifolds Of Computational Intelligence: Pattern Recognition, Uniform Approximation, Constrained Optimization, Np-hard Problems, And Image Segmentation. By Presenting Various Computational Models, This Book Is Developed To Provide Readers With A Quick But Insightful Understanding Of The Broad And Rapidly Growing Areas In The Neural Networks Domain.

besides Laying Down Fundamentals On Artificial Neural Networks, This Book Also Studies Biologically Inspired Neural Networks. Some Typical Computational Models Are Discussed, And Subsequently Applied To Objection Recognition, Scene Analysis And Associative Memory. The Studies Of Bio-inspired Models Have Important Implications In Computer Vision And Robotic Navigation, As Well As New Efficient Algorithms For Image Analysis. Another Significant Feature Of The Book Is That It Begins With Fundamental Dynamical Problems In Presenting The Mathematical Techniques Extensively Used In Analyzing Neurodynamics, Thus Allowing Non-mathematicians To Develop And Apply These Analytical Techniques Easily.

written For A Wide Readership, Engineers, Computer Scientists And Mathematicians Interested In Machine Learning, Data Mining And Neural Networks Modeling Will Find This Book Of Value. This Book Will Also Act As A Helpful Reference For Graduate Students Studying Neural Networks And Complex Dynamical Systems.

Neural Networks: Computational Models and Applications covers a wealth of important theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational intelligence: pattern recognition, uniform approximation, constrained optimization, NP-hard problems, and image segmentation. By presenting various computational models, this book is developed to provide readers with a quick but insightful understanding of the broad and rapidly growing areas in the neural networks domain. Besides laying down fundamentals on artificial neural networks, this book also studies biologically inspired neural networks. Some typical computational models are discussed, and subsequently applied to objection recognition, scene analysis and associative memory. The studies of bio-inspired models have important implications in computer vision and robotic navigation, as well as new efficient algorithms for image analysis. Another significant feature of the book is that it begins with fundamental dynamical problems in presenting the mathematical techniques extensively used in analyzing neurodynamics, thus allowing non-mathematicians to develop and apply these analytical techniques easily. Written for a wide readership, engineers, computer scientists and mathematicians interested in machine learning, data mining and neural networks modeling will find this book of value. This book will also act as a helpful reference for graduate students studying neural networks and complex dynamical systems.

one Of The Main Difficulties Of Applying An Evolutionary Algorithm (or, As A Matter Of Fact, Any Heuristic Method) To A Given Problem Is To Decide On An Appropriate Set Of Parameter Values. Typically These Are Specified Before The Algorithm Is Run And Include Population Size, Selection Rate, Operator Probabilities, Not To Mention The Representation And The Operators Themselves. This Book Gives The Reader A Solid Perspective On The Different Approaches That Have Been Proposed To Automate Control Of These Parameters As Well As Understanding Their Interactions. The Book Covers A Broad Area Of Evolutionary Computation, Including Genetic Algorithms, Evolution Strategies, Genetic Programming, Estimation Of Distribution Algorithms, And Also Discusses The Issues Of Specific Parameters Used In Parallel Implementations, Multi-objective Evolutionary Algorithms, And Practical Consideration For Real-world Applications. It Is A Recommended Read For Researchers And Practitioners Of Evolutionary Computation And Heuristic Methods.

"Neural Networks: Computational Models and Applications covers a wealth of important theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational intelligence: pattern recognition, uniform approximation, constrained optimization, NP-hard problems, and image segmentation, By presenting various computational models, this book is developed to provide leaders with a quick but insightful understanding of the broad and rapidly growing areas in the neural networks domain." "Written for a wide readership, engineers, computer scientists and mathematicians interested in machine learning, data mining and neural networks modeling will find this book of value. This book will also act as a helpful reference for graduate students studying neural networks and complex dynamical systems."--Jacket