Suzuki K. (ed.) Artificial Neural Networks - Industrial and Control Engineering Applications

InTech, 2011. — 490 p.Artificial neural networks may probably be the single most successful technology in the last two decades which has been widely used in a large variety of applications in various areas. An artificial neural network, oft en just called a neural network, is a mathematical (or computational) model that is inspired by the structure and function of biological neural networks in the brain. An artificial neural network consists of a number of artificial neurons (i.e., nonlinear processing units) which are connected each other via synaptic weights (or simply just weights). An artificial neural network can learn a task by adjusting weights. There are supervised and unsupervised models.
A supervised model requires a teacher or desired (ideal) output to learn a task. An unsupervised model does not require a teacher, but it leans a task based on a cost function associated with the task. An artificial neural network is a powerful, versatile tool. Artificial neural networks have been successfully used in various applications such as biological, medical, industrial, control engendering, software engineering, environmental, economical, and social applications. The high versatility of artificial neural networks comes from its high capability and learning function. It has been theoretically proved that an artificial neural network can approximate any continuous mapping by arbitrary precision. Desired continuous mapping or a desired task is acquired in an artificial neural network by learning.
The purpose of this book series is to provide recent advances of artificial neural network applications in a wide range of areas. The series consists of two volumes: the first volume contains methodological advances and biomedical applications of artificial neural networks; the second volume contains artificial neural network applications in industrial and control engineering. This second volume begins with a part of artificial neural network applications in textile industries which are concerned with the design and manufacture of clothing as well as the distribution and use of textiles. The part contains a review of various applications of artificial neural networks in textile and clothing industries as well as particular applications. A part of materials science and industry follows. This part contains applications of artificial neural networks in material identification, and estimation of material property, behavior, and state. Parts continue with food industry such as meat, electric and power industry such as batteries, power systems, and power allocation systems, mechanical engineering such as engines and machines, control and robotic engineering such as nonlinear system control, induction motors, system identification, signal and fault diagnosis systems, and robot manipulation.
Thus, this book will be a fundamental source of recent advances and applications of artificial neural networks in industrial and control engineering areas. The target audience of this book includes professors, college students, and graduate students in engineering schools, and engineers and researchers in industries. I hope this book will be a useful source for readers and inspire them.