Practical Course of Artificial Neural Networks with Real Life Examples

What are artificial neural networks?

An artificial neural network (ANN) is the piece of a computing device designed to simulate the way the human mind analyzes and procedures information. It is the inspiration of artificial intelligence (AI) and solves issues that might show not possible or tough through human or statistical standards. ANNs have self-gaining knowledge of skills that allow them to provide higher effects as extra records will become available.

Description:

Download Artificial Neural Networks PDF notes free from here. This notes is very useful any help full for those who interested in it and wants to learn more about neural networks, machine learning and artificial intelligence. In this notes you will learn the process of neural networks, its brief introduction and performance of parameters. This notes turned into written with the number one subject of answering readers with exclusive profiles, from those interested by acquiring understanding approximately architectures of artificial neural network to the ones inspired through its multiple applications for solving real-world problems.

This notes divided into two parts:

1. Architectures of Artificial Neural Networks and Their Theoretical Aspects
2. Application of Artificial Neural Networks in Engineering and Applied Science Problems

You Can Learn These Topics

Part 1:

Introduction

Fundamental Theory

Key Features

Biological Neuron

Artificial Neural Networks Architecture and Training Process

The Perceptron Network

Operating Principle of the Perceptron

The ADALINE Network and Delta Rule

Multiplayer Perceptron Network

Radial Basis Function Networks

Recurrent Hopfield Network

Self-Organizing Kohonen Network

LVQ and Counter Propagation Network

Adaptive Resonance Principle

Practical Work

Part 2:

Coffee Comprehensive Quality Estimate using Multiplayer Perceptron

Computer Networks Traffic Analysis Using SNNP Protocol and LVQ Networks

Forecast of Stock Market Trend Using Recurrent Networks

Computational Results

Recurrent Network Characteristics

Pattern Identification of Adulterants in Coffee Powders Using Kohonen Self-Organizing Map

Recognition of Disturbance Related to Electrical Power Quality Using MLP Networks

Characteristic of the MLP Networks

introduction

Characteristic of the Neural Network

Performance Analysis of RBF and MLP Networks in Pattern Classification

Solution of Constraint Optimization Problems Using Hopfield Networks

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