Principles Of Neurocomputing For Science And Engineering

This exciting new text covers artificial neural networks, but more specifically, neurocomputing. Neurocomputing is concerned with processing information, which involves a learning process within an artificial neural network architecture. This neural architecture responds to inputs according to a defined learning rule and then the trained network can be used to perform certain tasks depending on the application. Neurocomputing can play an important role in solving certain problems such as pattern recognition, optimization, event classification, control and identification of nonlinear systems, and statistical analysis. "Principles of Neurocomputing for Science and Engineering," unlike other neural networks texts, is written specifically for scientists and engineers who want to apply neural networks to solve complex problems. For each neurocomputing concept, a solid mathematical foundation is presented along with illustrative examples to accompany that particular architecture and associated training algorithm. The book is primarily intended for graduate-level neural networks courses, but in some instances may be used at the undergraduate level. The book includes many detailed examples and an extensive set of end-of-chapter problems.

Neurocomputing can be applied to problems such as pattern recognition, optimization, event classification, control and identification of nonlinear systems, and statistical analysis - just to name a few. This book is intended for a course in neural networks."--BOOK JACKET.

This is Volume III of a three volume set constituting the refereed proceedings of the Third International Symposium on Neural Networks, ISNN 2006. 616 revised papers are organized in topical sections on neurobiological analysis, theoretical analysis, neurodynamic optimization, learning algorithms, model design, kernel methods, data preprocessing, pattern classification, computer vision, image and signal processing, system modeling, robotic systems, transportation systems, communication networks, information security, fault detection, financial analysis, bioinformatics, biomedical and industrial applications, and more.

Materials science includes those parts of chemistry and physics that deal with the properties of materials. It encompasses four classes of materials, the study of each of which may be considered a separate field: metals; ceramics; polymers and composites. Materials science is often referred to as materials science and engineering because it has many applications. Industrial applications of materials science include processing techniques (casting, rolling, welding, ion implantation, crystal growth, thin-film deposition, sintering, glassblowing, etc.), analytical techniques (electron microscopy, x-ray diffraction, calorimetry, nuclear microscopy (HEFIB) etc.), materials design, and cost/benefit tradeoffs in industrial production of materials. This book presents new research directions in a very new field which happens to be an old field as well.

Written by authorities in the subject, this book provides a complete treatment of metal forming and machining by using the computational techniques FEM, fuzzy set theory and neural networks as modelling tools. The algorithms and solved examples included make this book of value to postgraduates, senior undergraduates, and lecturers and researchers in these fields. Research and development engineers and consultants for the manufacturing industry will also find it of use.

This volume contains the papers presented at the Second International Conference on Frontiers in Intelligent Computing: Theory and Applications (FICTA-2013) held during 14-16 November 2013 organized by Bhubaneswar Engineering College (BEC), Bhubaneswar, Odisha, India. It contains 63 papers focusing on application of intelligent techniques which includes evolutionary computation techniques like genetic algorithm, particle swarm optimization techniques, teaching-learning based optimization etc for various engineering applications such as data mining, Fuzzy systems, Machine Intelligence and ANN, Web technologies and Multimedia applications and Intelligent computing and Networking etc.

The two volume set LNCS 4984 and LNCS 4985 constitutes the thoroughly refereed post-conference proceedings of the 14th International Conference on Neural Information Processing, ICONIP 2007, held in Kitakyushu, Japan, in November 2007, jointly with BRAINIT 2007, the 4th International Conference on Brain-Inspired Information Technology. The 228 revised full papers presented were carefully reviewed and selected from numerous ordinary paper submissions and 15 special organized sessions. The 116 papers of the first volume are organized in topical sections on computational neuroscience, learning and memory, neural network models, supervised/unsupervised/reinforcement learning, statistical learning algorithms, optimization algorithms, novel algorithms, as well as motor control and vision. The second volume contains 112 contributions related to statistical and pattern recognition algorithms, neuromorphic hardware and implementations, robotics, data mining and knowledge discovery, real world applications, cognitive and hybrid intelligent systems, bioinformatics, neuroinformatics, brain-conputer interfaces, and novel approaches.

Most data from satellites are in image form, thus most books in the remote sensing field deal exclusively with image processing. However, signal processing can contribute significantly in extracting information from the remotely sensed waveforms or time series data. Pioneering the combination of the two processes, Signal and Image Processing for Remote Sensing provides a balance between the role of signal processing and image processing in remote sensing. Featuring contributions from worldwide experts, this book emphasizes mathematical approaches. Divided into two parts, Part I examines signal processing for remote sensing and Part II explores image processing. Not limited to the problems with data from satellite sensors, the book considers other sensors which acquire data remotely, including signals and images from infrasound, seismic, microwave, and satellite sensors. It covers a broader scope of issues in remote sensing information processing than other books in this area. With rapid technological advances, the mathematical techniques provided will far outlast the sensor, software and hardware technologies. Focusing on methodologies of signal processing and image processing in remote sensing, this book discusses unique techniques for dealing with remote sensing problems.

Recent developments in information processing systems have driven the advancement of computational methods in the engineering realm. New models and simulations enable better solutions for problem-solving and overall process improvement. The Handbook of Research on Advanced Computational Techniques for Simulation-Based Engineering is an authoritative reference work representing the latest scholarly research on the application of computational models to improve the quality of engineering design. Featuring extensive coverage on a range of topics from various engineering disciplines, including, but not limited to, soft computing methods, comparative studies, and hybrid approaches, this book is a comprehensive reference source for students, professional engineers, and researchers interested in the application of computational methods for engineering design.