In order to develope new types of information media and technology, it is essential to model complex and flexible information processing in living systems. This book presents a new approach to modeling complex information processing in living systems. Traditional information-theoretic methods in neural networks are unified in one framework, i.e. a-entropy. This new approach will enable information systems such as computers to imitate and simulate human complex behavior and to uncover the deepest secrets of the human mind.

The potential value of artificial neural networks (ANN) as a predictor of malignancy has begun to receive increased recognition. Research and case studies can be found scattered throughout a multitude of journals. Artificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management brings together the work of top researchers - primarily clinicians - who present the results of their state-of-the-art work with ANNs as applied to nearly all major areas of cancer for diagnosis, prognosis, and management of the disease.

The book introduces the theory of neural networks and the method of their application in oncology. It is not an exercise in ANN research, but the presentation of a new technique for diagnosing and determining the treatment of cancers. The authors have included almost all cancers for which there exist ANN applications. When the data available is ill-defined and the development of an algorithmic solution difficult, neural networks provide a non-linear approach which helps sift through the maze of information and arrive at a reasonable solution.

Highly interdisciplinary in nature, this book provides comprehensive coverage of the most important materials relating to the applications of ANNs in the cancer field. With contributions from prominent research centers worldwide, it serves as an introduction to how neural networks can be used for accurate prediction or diagnosis and shows why neural networks are more accurate. Artificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management gives you an understanding of this new tool, its applications, and when it should be used.

Artificial neural networks can mimic the biological information-processing mechanism in - a very limited sense. Fuzzy logic provides a basis for representing uncertain and imprecise knowledge and forms a basis for human reasoning. Neural networks display genuine promise in solving problems, but a definitive theoretical basis does not yet exist for their design.
Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms integrates neural net, fuzzy system, and evolutionary computing in system design that enables its readers to handle complexity - offsetting the demerits of one paradigm by the merits of another.
This book presents specific projects where fusion techniques have been applied. The chapters start with the design of a new fuzzy-neural controller. Remaining chapters discuss the application of expert systems, neural networks, fuzzy control, and evolutionary computing techniques in modern engineering systems. These specific applications include:
o direct frequency converters
o electro-hydraulic systems
o motor control
o toaster control
o speech recognition
o vehicle routing
o fault diagnosis
o Asynchronous Transfer Mode (ATM) communications networks
o telephones for hard-of-hearing people
o control of gas turbine aero-engines
o telecommunications systems design
Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms covers the spectrum of applications - comprehensively demonstrating the advantages of fusion techniques in industrial applications.

The Handbook of Neural Computation is a practical, hands-on guide to the design and implementation of neural networks used by scientists and engineers to tackle difficult and/or time-consuming problems.
The handbook bridges an information pathway between scientists and engineers in different disciplines who apply neural networks to similar problems. It is unmatched in the breadth of its coverage and is certain to become the standard reference resource for the neural network community.

This concise, readable book provides a sampling of the very large, active, and expanding field of artificial neural network theory. It considers select areas of discrete mathematics linking combinatorics and the theory of the simplest types of artificial neural networks. Neural networks have emerged as a key technology in many fields of application, and an understanding of the theories concerning what such systems can and cannot do is essential. The author discusses interesting connections between special types of Boolean functions and the simplest types of neural networks. Some classical results are presented with accessible proofs, together with some more recent perspectives, such as those obtained by considering decision lists. In addition, probabilistic models of neural network learning are discussed. Graph theory, some partially ordered set theory, computational complexity, and discrete probability are among the mathematical topics involved. Pointers to further reading and an extensive bibliography make this book a good starting point for research in discrete mathematics and neural networks.

Originating from models of biological neural systems, artificial neural networks (ANN) are the cornerstones of artificial intelligence research. Catalyzed by the upsurge in computational power and availability, and made widely accessible with the co-evolution of software, algorithms, and methodologies, artificial neural networks have had a profound impact in the elucidation of complex biological, chemical, and environmental processes. Artificial Neural Networks in Biological and Environmental Analysis provides an in-depth and timely perspective on the fundamental, technological, and applied aspects of computational neural networks. Presenting the basic principles of neural networks together with applications in the field, the book stimulates communication and partnership among scientists in fields as diverse as biology, chemistry, mathematics, medicine, and environmental science. This interdisciplinary discourse is essential not only for the success of independent and collaborative research and teaching programs, but also for the continued interest in the use of neural network tools in scientific inquiry. The book covers: A brief history of computational neural network models in relation to brain function Neural network operations, including neuron connectivity and layer arrangement Basic building blocks of model design, selection, and application from a statistical perspective Neurofuzzy systems, neuro-genetic systems, and neuro-fuzzy-genetic systems Function of neural networks in the study of complex natural processes Scientists deal with very complicated systems, much of the inner workings of which are frequently unknown to researchers. Using only simple, linear mathematical methods, information that is needed to truly understand natural systems may be lost. The development of new algorithms to model such processes is needed, and ANNs can play a major role. Balancing basic principles and diverse applications, this text introduces newcomers to the field and reviews recent developments of interest to active neural network practitioners.

The use of genetic algorithms as a training method for neural networks is described in this book. After introducing neural networks and genetic algorithms, it gives a number of examples to demonstrate the use of the proposed techniques. Moreover, a comparison of the results with the back-propagation algorithm is made.

The Proceedings cover a wide range of topics: theoretical foundations of neural nets, novel neural net architectures, biological information processing, discrete fluid models, learning cellular automata, electronic and optoelectronic implementation of neural nets and cellular automata, and applications of neural nets to pattern and speech recognition.