Applied Neural Networks and Soft Computing

Soft computing is a partnership of computing techniques. The partnership includes fuzzy logic, Artificial Neural Networks (ANNs), and genetic algorithms. Conventionally, a huge set of techniques are referred to as hard computing, such as stochastic and statistical methods bound by the concept called NP (verifiable in Nondeterministic Polynomial time)-complete. Unlike hard computing, soft computing techniques offer "inexact" solutions of very complex problems through modeling and analysis with a tolerance of imprecision, uncertainty, partial truth, and approximation. In fact, soft computing is an integration of biological structures and computing techniques. In the partnership, ANNs provides configurations made up of interconnecting artificial neurons that mimic the properties of biological neurons. Soft computing (SC) is a branch, in which, it is tried to build intelligent and wiser machines. Intelligence provides the power to derive the answer and not simply arrive to the answer. Purity of thinking, machine intelligence, freedom to work, dimensions, complexity and fuzziness handling capability increase. Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on back propagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohonen self-organizing network. These networks, especially the multilayer perceptron network with a backpropagation training algorithm, have gained recognition in research and applications in various scientific and engineering areas. Applied Neural Networks and Soft Computing presents new trends and developments in soft computing techniques. Topics include: neural networks, fuzzy systems, evolutionary computation, knowledge discovery, rough sets, and hybrid methods. It also covers various applications of soft computing techniques in economics, mechanics, medicine, automatics and image processing. Recurrent neural networks can also be used in virtual reality and nonlinear dynamical systems. Soft computing is a partnership of computing techniques. The partnership includes fuzzy logic, Artificial Neural Networks (ANNs), and genetic algorithms. Conventionally, a huge set of techniques are referred to as hard computing, such as stochastic and statistical methods bound by the concept called NP (verifiable in Nondeterministic Polynomial time)-complete. Unlike hard computing, soft computing techniques offer "inexact" solutions of very complex problems through modeling and analysis with a tolerance of imprecision, uncertainty, partial truth, and approximation. In fact, soft computing is an integration of biological structures and computing techniques. In the partnership, ANNs provides configurations made up of interconnecting artificial neurons that mimic the properties of biological neurons. Soft computing (SC) is a branch, in which, it is tried to build intelligent and wiser machines. Intelligence provides the power to derive the answer and not simply arrive to the answer. Purity of thinking, machine intelligence, freedom to work, dimensions, complexity and fuzziness handling capability increase. Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on back propagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohonen self-organizing network. These networks, especially the multilayer perceptron network with a backpropagation training algorithm, have gained recognition in research and applications in various scientific and engineering areas. Applied Neural Networks and Soft Computing presents new trends and developments in soft computing techniques. Topics include: neural networks, fuzzy systems, evolutionary computation, knowledge discovery, rough sets, and hybrid methods. It also covers various applications of soft computing techniques in economics, mechanics, medicine, automatics and image processing. Recurrent neural networks can also be used in virtual reality and nonlinear dynamical systems. Soft computing is a partnership of computing techniques. The partnership includes fuzzy logic, Artificial Neural Networks (ANNs), and genetic algorithms. Conventionally, a huge set of techniques are referred to as hard computing, such as stochastic and statistical methods bound by the concept called NP (verifiable in Nondeterministic Polynomial time)-complete. Unlike hard computing, soft computing techniques offer "inexact" solutions of very complex problems through modeling and analysis with a tolerance of imprecision, uncertainty, partial truth, and approximation. In fact, soft computing is an integration of biological structures and computing techniques. In the partnership, ANNs provides configurations made up of interconnecting artificial neurons that mimic the properties of biological neurons. Soft computing (SC) is a branch, in which, it is tried to build intelligent and wiser machines. Intelligence provides the power to derive the answer and not simply arrive to the answer. Purity of thinking, machine intelligence, freedom to work, dimensions, complexity and fuzziness handling capability increase. Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on back propagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohonen self-organizing network. These networks, especially the multilayer perceptron network with a backpropagation training algorithm, have gained recognition in research and applications in various scientific and engineering areas. Applied Neural Networks and Soft Computing presents new trends and developments in soft computing techniques. Topics include: neural networks, fuzzy systems, evolutionary computation, knowledge discovery, rough sets, and hybrid methods. It also covers various applications of soft computing techniques in economics, mechanics, medicine, automatics and image processing. Recurrent neural networks can also be used in virtual reality and nonlinear dynamical systems.