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"As an extension of artificial intelligence research, artificial neural networks (ANN) aim to simulate intelligent behavior by mimicking the way that biological neural networks function. In Artificial Neural Networks, an international panel of experts report the history of the application of ANN to chemical and biological problems, provide a guide to network architectures, training and the extraction of rules from trained networks, and cover many cutting-edge examples of the application of ANN to chemistry and biology. In the tradition of the highly successful Methods in Molecular Biology series, this volume exhibits clear, easy-to-use information with many step-by-step laboratory protocols."

"This SpringerBrief focuses on modeling and power evaluation of high-speed craft. The various power prediction methods, a principal design objective for high-speed craft of displacement, semi-displacement, and planing type, are addressed. At the core of the power prediction methods are mathematical models for resistance and propulsion efficiency. The models are based on the experimental data of various high-speed hull and propeller series. The regression analysis and artificial neural network (ANN) methods are used as an extraction tool for this kind of mathematical models. A variety of mathematical models of this type are discussed in the book.Once these mathematical models have been developed and validated, they can be readily programmed into software tools, thereby enabling the parametric analyses required for the optimization of a high-speed craft design. This book provides the foundational reference for these software tools, and their use in the design of high-speed craft. High-speed craft are very different from conventional ships. Current professional literature leaves a gap in the documentation of best design practices for high-speed craft. This book is aimed at naval architects who design and develop various types of high-speed vessels."

"Jaringan Saraf Tiruan (JST) merupakan suatu model matematis yang dewasa ini banyak digunakan dan algoritma pembelajarannya merupakan hal yang menarik untuk dipelajari. Pada skripsi ini, akan dibahas mengenai JST berbasis Radial Basis Function (RBF) dan perbandingannya dengan JST berbasis backpropagation yang dewasa ini banyak digunakan. Optimasi JST ensemble dengan algoritma Negative Correlation Learning (NCL) berbasis RBF juga akan dilakukan pada skripsi ini. Set data yang akan digunakan selama percobaan adalah data UC Irvine Machine Learning Repository (UCI) dan citra manusia cahaya tampak dan infra merah.

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Artificial neural network is a mathematical model that nowadays has been applied widely and its learning algorithm has become an interesting object to learn. This thesis is going to discuss artificial neural network based on Radial Basis Function (RBF) and its comparison with backpropagation that has been widely purposed. Afterward, optimation is conducted in term of ensemble artificial neural network with Negative Correlation Learning (NCL) algorithm based on RBF. The databases to use are UC Irvine Machine Learning Repository (UCI) and pictures of human face that are achieved from infra-red and visible-light cameras. "

"This book is to presents and evaluates a way of modelling and optimizing nonlinear RFID Network Planning (RNP) problems using artificial intelligence techniques. It uses Artificial Neural Network models (ANN) to bind together the computational artificial intelligence algorithm with knowledge representation an efficient artificial intelligence paradigm to model and optimize RFID networks.This effort leads to proposing a novel artificial intelligence algorithm which has been named hybrid artificial intelligence optimization technique to perform optimization of RNP as a hard learning problem. This hybrid optimization technique consists of two different optimization phases. First phase is optimizing RNP by Redundant Antenna Elimination (RAE) algorithm and the second phase which completes RNP optimization process is Ring Probabilistic Logic Neural Networks (RPLNN).The hybrid paradigm is explored using a flexible manufacturing system (FMS) and the results are compared with well-known evolutionary optimization technique namely Genetic Algorithm (GA) to demonstrate the feasibility of the proposed architecture successfully."

"This book highlights the rapidly developing field of advanced optical methods for structural and functional brain imaging. As is known, the brain is the most poorly understood organ of a living body. It is indeed the most complex structure in the known universe and, thus, mapping of the brain has become one of the most exciting frontlines of contemporary research. Starting from the fundamentals of the brain, neurons and synapses, this book presents a streamlined and focused coverage of the core principles, theoretical and experimental approaches, and state-of-the-art applications of most of the currently used imaging methods in brain research. It presents contributions from international leaders on different photonics-based brain imaging modalities and techniques. Included are comprehensive descriptions of many of the technology driven spectacular advances made over the past few years that have allowed novel insights of the structural and functional details of neurons.The book is targeted at researchers, engineers and scientists who are working in the field of brain imaging, neuroscience and connectomics. Although this book is not intended to serve as a textbook, it will appeal to undergraduate students engaged in the specialization of brain imaging."