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Artificial Higher Order Neural Networks for Computer Science and Engineering: Trends for Emerging Applications

Artificial Higher Order Neural Networks for Computer Science and Engineering: Trends for Emerging Applications

Copyright: © 2010 |Pages: 660

ISBN13: 9781615207114|ISBN10: 1615207112|EISBN13: 9781615207121

DOI: 10.4018/978-1-61520-711-4

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MLA

Zhang, Ming , editor. Artificial Higher Order Neural Networks for Computer Science and Engineering: Trends for Emerging Applications. IGI Global, 2010. https://doi.org/10.4018/978-1-61520-711-4

APA

Zhang, M. (Ed.). (2010). Artificial Higher Order Neural Networks for Computer Science and Engineering: Trends for Emerging Applications. IGI Global. https://doi.org/10.4018/978-1-61520-711-4

Chicago

Zhang, Ming , ed. Artificial Higher Order Neural Networks for Computer Science and Engineering: Trends for Emerging Applications. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-61520-711-4

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Artificial neural network research is one of the promising new directions for the next generation of computers and open box artificial Higher Order Neural Networks (HONNs) play an important role in this future.

Artificial Higher Order Neural Networks for Computer Science and Engineering: Trends for Emerging Applications introduces Higher Order Neural Networks (HONNs) to computer scientists and computer engineers as an open box neural networks tool when compared to traditional artificial neural networks. Since HONNs are open box models, they can be easily used in information science, information technology, management, economics, and business. This book details the techniques, theory and applications essential to engaging and capitalizing on this developing technology.

Table of Contents

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Front Materials

Title Page

Copyright Page

Dedication

List of Reviewers

Preface

Acknowledgment

Chapters

Artificial Higher Order Neural Networks for Computer Science

Chapter 1

Higher Order Neural Network Group-based Adaptive Tolerance Trees (pages 1-36)

Ming Zhang

Recent artificial higher order neural network research has focused on simple models, but such models have not been very successful in describing complex systems (such as face recognition). This chapter presents the artificial Higher...

Chapter 2

Higher Order Neural Networks for Symbolic, Sub-symbolic and Chaotic Computations (pages 37-56)

João Pedro Neto

This chapter deals with discrete and recurrent artificial neural networks with a homogenous type of neuron. With this architecture we will see how to perform symbolic computations by executing high-level programs within this network...

Chapter 3

Evolutionary Algorithm Training of Higher Order Neural Networks (pages 57-85)

M. G. Epitropakis, V. P. Plagianakos, Michael N. Vrahatis

This chapter aims to further explore the capabilities of the Higher Order Neural Networks class and especially the Pi-Sigma Neural Networks. The performance of Pi-Sigma Networks is evaluated through several well known neural network...

Chapter 4

Adaptive Higher Order Neural Network Models for Data Mining (pages 86-98)

Shuxiang Xu

Data mining, the extraction of hidden patterns and valuable information from large databases, is a powerful technology with great potential to help companies survive competition. Data mining tools search databases for hidden...

Chapter 5

Robust Adaptive Control Using Higher Order Neural Networks and Projection (pages 99-137)

Wen Yu

By using dynamic higher order neural networks, we present a novel robust adaptive approach for a class of unknown nonlinear systems. Firstly, the neural networks are designed to identify the nonlinear systems. Dead-zone and...

Chapter 6

On the Equivalence between Ordinary Neural Networks and Higher Order Neural Networks (pages 138-158)

Mohammed Sadiq Al-Rawi, Kamal R. Al-Rawi

In this chapter, we study the equivalence between multilayer feedforward neural networks referred as Ordinary Neural Networks (ONNs) that contain only summation (Sigma) as activation units, and multilayer feedforward Higher order...

Artificial Higher Order Neural Networks for Simulation and Modeling

Chapter 7

Rainfall Estimation Using Neuron-Adaptive Higher Order Neural Networks (pages 159-186)

Ming Zhang

Real world data is often nonlinear, discontinuous and may comprise high frequency, multi-polynomial components. Not surprisingly, it is hard to find the best models for modeling such data. Classical neural network models are unable...

Chapter 8

Analysis of Quantization Effects on Higher Order Function and Multilayer Feedforward Neural Networks (pages 187-222)

Minghu Jiang, Georges Gielen, Lin Wang

In this chpater we investigate the combined effects of quantization and clipping on Higher Order function neural networks (HOFNN) and multilayer feedforward neural networks (MLFNN). Statistical models are used to analyze the effects...

Chapter 9

Improving Sparsity in Kernelized Nonlinear Feature Extraction Algorithms by Polynomial Kernel Higher Order Neural Networks (pages 223-238)

Zhao Lu, Gangbing Song, Leang-san Shieh

As a general framework to represent data, the kernel method can be used if the interactions between elements of the domain occur only through inner product. As a major stride towards the nonlinear feature extraction and dimension...

Chapter 10

Analysis and Improvement of Function Approximation Capabilities of Pi-Sigma Higher Order Neural Networks (pages 239-254)

Junichi Murata

A Pi-Sigma higher order neural network (Pi-Sigma HONN) is a type of higher order neural network, where, as its name implies, weighted sums of inputs are calculated first and then the sums are multiplied by each other to produce...

Chapter 11

Dynamic Ridge Polynomial Higher Order Neural Network (pages 255-268)

Rozaida Ghazali, Abir Hussain, Nazri Mohd Nawi

This chapter proposes a novel Dynamic Ridge Polynomial Higher Order Neural Network (DRPHONN). The architecture of the new DRPHONN incorporates recurrent links into the structure of the ordinary Ridge Polynomial Higher Order Neural...

Artificial Higher Order Neural Networks for Computer Engineering

Chapter 12

Fifty years of Electronic Hardware Implementations of First and Higher Order Neural Networks (pages 269-285)

David R. Selviah, Janti Shawash

This chapter celebrates 50 years of first and higher order neural network (HONN) implementations in terms of the physical layout and structure of electronic hardware, which offers high speed, low latency, compact, low cost, low...

Chapter 13

Recurrent Higher Order Neural Network Control for Output Trajectory Tracking with Neural Observers and Constrained Inputs (pages 286-311)

Luis J. Ricalde, Edgar N. Sanchez, Alma Y. Alanis

This Chapter presents the design of an adaptive recurrent neural observer-controller scheme for nonlinear systems whose model is assumed to be unknown and with constrained inputs. The control scheme is composed of a neural observer...

Chapter 14

Artificial Higher Order Neural Network Training on Limited Precision Processors (pages 312-332)

Janti Shawash, David R. Selviah

Previous research suggested Artificial Neural Network (ANN) operation in a limited precision environment was particularly sensitive to the precision and could not take place below a certain threshold level of precision. This study...

Chapter 15

Recurrent Higher Order Neural Observers for Anaerobic Processes (pages 333-365)

Edgar N. Sanchez, Diana V. Urrego, Alma Y. Alanis, Salvador Carlos-Hernandez

In this chapter we propose the design of a discrete-time neural observer which requires no prior knowledge of the model of an anaerobic process, for estimate biomass, substrate and inorganic carbon which are variables difficult to...

Chapter 16

Electric Machines Excitation Control via Higher Order Neural Networks (pages 366-396)

Yannis L. Karnavas

This chapter is demonstrating a practical design of an intelligent type of controller using higher order neural network (HONN) concepts, for the excitation control of a practical power generating system. This type of controller is...

Artificial Higher Order Neural Network Models and Applications

Chapter 17

Higher Order Neural Networks: Fundamental Theory and Applications (pages 397-422)

Madan M. Gupta, Noriyasu Homma, Zeng-Guang Hou, Ashu M. G. Solo, Ivo Bukovsky

In this chapter, we provide fundamental principles of higher order neural units (HONUs) and higher order neural networks (HONNs). An essential core of HONNs can be found in higher order weighted combinations or correlations between...

Chapter 18

Identification of Nonlinear Systems Using a New Neuro-Fuzzy Dynamical System Definition Based on High Order Neural Network Function Approximators (pages 423-449)

Yiannis S. Boutalis, M. A. Christodoulou, Dimitris C. Theodoridis

A new definition of adaptive dynamic fuzzy systems (ADFS) is presented in this chapter for the identification of unknown nonlinear dynamical systems. The proposed scheme uses the concept of adaptive fuzzy systems operating in...

Chapter 19

Neuro – Fuzzy Control Schemes Based on High Order Neural Network Function Approximators (pages 450-483)

Dimitris C. Theodoridis, M. A. Christodoulou, Yiannis S. Boutalis

The indirect or direct adaptive regulation of unknown nonlinear dynamical systems is considered in this chapter. Since the plant is considered unknown, we first propose its approximation by a special form of a fuzzy dynamical system...

Chapter 20

Back-Stepping Control of Quadrotor: A Dynamically Tuned Higher Order Like Neural Network Approach (pages 484-513)

Abhijit Das, Frank L. Lewis, Kamesh Subbarao

The dynamics of a quadrotor is a simplified form of helicopter dynamics that exhibit the same basic problems of strong coupling, multi-input/multi-output design, and unknown nonlinearities. The Lagrangian model of a typical quadrotor...

Chapter 21

Artificial Tactile Sensing and Robotic Surgery Using Higher Order Neural Networks (pages 514-544)

Siamak Najarian, Sayyed Mohsen Hosseini, Mehdi Fallahnezhad

In this chapter, a new medical instrument, namely, the Tactile Tumor Detector (TTD) able to simulate the sense of touch in clinical and surgical applications is introduced. All theoretical and experimental attempts for its...

Chapter 22

A Theoretical and Empirical Study of Functional Link Neural Networks (FLANNs) for Classification (pages 545-573)

Satchidananda Dehuri, Sung-Bae Cho

In this chapter, the primary focus is on theoretical and empirical study of functional link neural networks (FLNNs) for classification. We present a hybrid Chebyshev functional link neural network (cFLNN) without hidden layer with...

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