Editor: Pierre Lorrentz

Artificial Neural Systems: Principles and Practice

eBook: US $29 Special Offer (PDF + Printed Copy): US $133
Printed Copy: US $119
Library License: US $116
ISBN: 978-1-68108-091-8 (Print)
ISBN: 978-1-68108-090-1 (Online)
Year of Publication: 2015
DOI: 10.2174/97816810809011150101


An intelligent system is one which exhibits characteristics including, but not limited to, learning, adaptation, and problem-solving. Artificial Neural Network (ANN) Systems are intelligent systems designed on the basis of statistical models of learning that mimic biological systems such as the human central nervous system. Such ANN systems represent the theme of this book. This book also describes concepts related to evolutionary methods, clustering algorithms, and other networks which are complementary to ANN systems.

The book is divided into two parts. The first part explains basic concepts derived from the natural biological neuron and introduces purely scientific frameworks used to develop a viable ANN model. The second part expands over to the design, analysis, performance assessment, and testing of ANN models. Concepts such as Bayesian networks, multi-classifiers, and neuromorphic ANN systems are explained, among others.

Artificial Neural Systems: Principles and Practice takes a developmental perspective on the subject of ANN systems, making it a beneficial resource for students undertaking graduate courses and research projects, and working professionals (engineers, software developers) in the field of intelligent systems design.


Neural Networks, Fuzzy Logic and Evolutionary Computing are members of Soft Computing class of techniques. The techniques are capable of identifying and handling inexact solutions for complex tasks and can deal with the real life uncertainties within the computational framework. Soft Computing has significantly matured over the years and we can find significant applications of soft computing in industry and research environment. Neural Networks is the most mature of other techniques in the Soft Computing. The networks have also benefitted from integrated Fuzzy Logic based systems to model complex engineering systems with the human expert knowledge and through robust system modelling.

Evolutionary Computing helps to optimise the design a Neural Network. Each members of the Soft Computing has several algorithms and concepts that need better understanding for application development.

This book on ‘artificial neural networks – principle and practice’ provides necessary foundation to understand the basics of Neural Networks and how to develop real life applications. From basic definitions to relevant theorems the book presents an algorithm approach to describe the foundations.

The book also emphasises systematic approach to Intelligent System analysis and design. In order to build a Neural Network or Artificial Neural Network application, one needs to apply knowledge of probability based methods as well as fuzzy sets for more uncertain aspects of the problem. The book then explains the motivation from our neural system to develop the Neural Networks.

Description of other network-based approaches using nodes and edges also strengthens the understanding about the Neural Networks. A major strength of the book is the fundamentals of quantum logic for emerging Neural Network development. This is major area for future development. A discussion on Neural Network hardware would have strengthened the book.

The second part of the book presents detailed discussion on learning algorithms, current and emerging Neural Network structures and application development. The chapters also present metrics to evaluate effectiveness of the network. Selection and integration of multiple Neural Networks to solve a real life and complex problem is a major aspect of the book. As mentioned before there are several algorithms and approaches to solve a problem, the systematic approach presented in the book is of major interest. Application of the network to solve a complex modelling task usually requires significant volume of data. A further discussion on the modelling approaches with less data would be very helpful.

The emphasis on probability based neural network and its application is significant because of its popularity. But the real strength of this part of the book is in describing the Quantum Neural Networks and the Deep Belief Network (DBN).

Finally the book also outlines the research and development in Neural Networks. Future Neural Networks are learning from specialised parts of our neural system and trying to scale up to solve even more complex engineering applications.

Rajkumar Roy
Cranfield University, UK


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