Multilayer Perceptrons: Theory and Applications


Ruth Vang-Mata (Editor)

Series: Computer Science, Technology and Applications
BISAC: COM014000

Multilayer Perceptrons: Theory and Applications opens with a review of research on the use of the multilayer perceptron artificial neural network method for solving ordinary/partial differential equations, accompanied by critical comments.

A historical perspective on the evolution of the multilayer perceptron neural network is provided. Furthermore, the foundation for automated post-processing that is imperative for consolidating the signal data to a feature set is presented.

In one study, panoramic dental x-ray images are used to estimate age and gender. These images were subjected to image pre-processing techniques to achieve better results.

In a subsequent study, a multilayer perceptrons artificial neural network with one hidden layer and trained through the efficient resilient backpropagation algorithm is used for modeling quasi-fractal patch antennas.

Later, the authors propose a scheme with eight steps for a dynamic time series forecasting using an adaptive multilayer perceptron with minimal complexity. Two different data sets from two different countries were used in the experiments to measure the robustness and accuracy of the models.

In closing, a multilayer perceptron artificial neural network with a layer of hidden neurons is trained with the resilient backpropagation algorithm, and the network is used to model a Koch pre-fractal patch antenna.
(Imprint: Nova)



Table of Contents


Chapter 1. Multilayer Perceptron Artificial Neural Network: A Review
(Akanksha Verma and Manoj Kumar, Department of Mathematics, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, Uttar Pradesh, India)

Chapter 2. Machine Learning Classification for Network Centric Therapy Utilizing the Multilayer Perceptron Neural Network
(Robert LeMoyne, PhD, and Timothy Mastroianni, Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, USA and Cognition Engineering, Pittsburgh, Pennsylvania, USA)

Chapter 3. Age Estimation by Using Multi-Layer Perceptron Neural Network with Image Processing Techniques
(Emre Avuçlu and Fatih Başçiftçi, Department of Computer Technology, Aksaray University, Aksaray, Turkey, and others)

Chapter 4. Dynamic Forecasting of Electric Load Consumption Using Adaptive Multilayer Perceptron (AMLP)
(Jeremias T. Lalis and Elmer A. Maravillas, College of Computer Studies, Cebu Institute of Technology, Cebu City, Philippines)

Chapter 5. Development of the Pre-Fractal Pach Antenna with Artificial Neural Network
(Elder Eldervitch Carneiro de Oliveira, Wellington Candeia de Araujo, Marcelo da Silva Vieira e Pedro Carlos de Assis Jr., Paulo Henrique da Fonseca Silva, Paulo Fernandes da Silva Junior, Ewaldo Eder Carvalho Santana, Daniel Matos Luna dos Santos and Tiago Santos Ferreira, Centro de Ciências Exatas e Sociais Aplicadas, Universidade Estadual da Paraíba, Patos, Paraiba, Brazil, and others)


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