Intelligent Computing A Computing Approach To Artificial Intelligence 15 Hon Keung Kwan by Hon Keung Kwan 9781988307091, 1988307090 instant download after payment.

The Edition1.5 of this eBook on Intelligent Computing gives a clear description of the theories, models, algorithms, and  design methodologies of intelligent computing to mimic and explain humanlike reasoning, learning, and memory. The eBook consists of 20 chapters organized into 5 parts.

The Part 1 on Intelligent Computing consists of Chapter 1 which first gives an overview of each of computational intelligence, artificial intelligence, intelligent computing, and intelligent system design before presenting an  introduction to the basics of biological and artificial neural networks.

The Part II on Neural Computing starts with an overview of neural computing and consists of Chapters 2 to 8 covering various neural networks, supervised and unsupervised learning algorithms,

The Part III on Fuzzy Neural Computing first provides an overview of fuzzy neural computing and consists of Chapter 9 to 12 explaining fuzzy set, fuzzy logic and reasoning, fuzzy systems, and fuzzy neural networks.

The Part IV on Evolutionary Computing begins with an overview of evolutionary computing and consists of Chapter 13 to 17 describing four popular evolutionary algorithms, genetic algorithms, differential evolution, particle swarm optimization, and simulated annealing: and aspects on coding, initialization, and constraints.

Finally, the Part V on Deep Neural Computing first presents an overview of deep neural computing and consists of three chapters on multiplierless neural networks, convolutional neural networks, and deep residual learning and networks. The eBook gives a comprehensive coverage of intelligent computing as a computing approach to artificial intelligence. The topics covered by this eBook are clearly described and explained with the aid of step-by-step hand-calculation examples. Matlab examples, Matlab design examples, equations, and self-explanatory figures and tables. 

This eBook can be used as a senior undergraduate and graduate textbook on Intelligent Computing for students in engineering, computer science, informatics, management science, and other programs. This eBook contains  fundamental and advanced topics to meet the curriculum requirements of both undergraduate and  graduate courses.

The eBook is also suitable for practicing engineers and computing professionals who wish to self-lean and understand intelligent computing as a computing approach to artificial intelligence, and to explore new  possibilities for research and practical projects. No specific prerequisites are required but a background of  elementary courses in linear algebra and calculus would be useful. 

This eBook is of great use to those who are interested in (A) the models and learning algorithms of neural networks, fuzzy systems, fuzzy neural networks» and deep neural networks; and (B) their design methods for a specific application from the problem formulation and parameter vector representation, to the use of backpropagation or evolutionary algorithms for training. 

This eBook is designed to be used together with an accompanying eBook written by the same author entitled  "Matlab and Worked Problems in Intelligent Computing and System Design" to reinforce and enrich learning. It  contains worked problems and assignment problems for the purposes of course assignments and self-learning. 

There are a total of 98 examples in which 37 of them are design examples. The design examples illustrate how  neural networks, fuzzy systems, fuzzy neural networks, and deep neural networks can be designed to build  intelligent systems. The eBook contains over 150 tables, 176 figures, 195 references, and a total of 721 pages to support and clarify explanations.