ANSARI - Artificial Neural Network: Learn About Electronics (Learn Electronics)

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Artificial Neural Network

Learn About Electronics

Contents

Preface

Neural networks are parallel computing devices, which are basically an attempt to make a computer model of the brain. The main objective is to develop a system to perform various computational tasks faster than the traditional systems. This book covers the basic concept and terminologies involved in Artificial Neural Network (ANN). Sections of this book also explain the architecture as well as the training algorithm of various networks used in ANN.

This book will be useful for graduates, post graduates, and research students who either have an interest in this subject or have this subject as a part of their curriculum. The reader can be a beginner or an advanced learner.

ANN is an advanced topic, hence the reader must have basic knowledge of Algorithms, Programming, and Mathematics.

Basic Concepts

Neural networks are parallel computing devices, which is basically an attempt to make a computer model of the brain. The main objective is to develop a system to perform various computational tasks faster than the traditional systems. These tasks include pattern recognition and classification, approximation, optimization, and data clustering.

What is Artificial Neural Network?

Artificial Neural Network ANN ANN is an efficient computing system whose central theme is borrowed from the analogy of biological neural networks. ANNs are also named as artificial neural systems, or parallel distributed processing systems, or connectionist systems. ANN acquires a large collection of units that are interconnected in some pattern to allow communication between the units. These units, also referred to as nodes or neurons, are simple processors which operate in parallel.

Every neuron is connected with other neuron through a connection link. Each connection link is associated with a weight that has information about the input signal. This is the most useful information for neurons to solve a particular problem because the weight usually excites or inhibits the signal that is being communicated. Each neuron has an internal state, which is called an activation signal. Output signals, which are produced after combining the input signals and activation rule, may be sent to other units.

A Brief History of ANN

The history of ANN can be divided into the following three eras

ANN during 1940s to 1960s

Some key developments of this era are as follows

• 1943 It has been assumed that the concept of neural network started with the work of physiologist, Warren McCulloch, and mathematician, Walter Pitts, when in 1943 they modeled a simple neural network using electrical circuits in order to describe how neurons in the brain might work.
• 1949 Donald Hebbs book, The Organization of Behavior , put forth the fact that repeated activation of one neuron by another increases its strength each time they are used.
• 1956 An associative memory network was introduced by Taylor.
• 1958 A learning method for McCulloch and Pitts neuron model named Perceptron was invented by Rosenblatt.
• 1960 Bernard Widrow and Marcian Hoff developed models called "ADALINE" and MADALINE.

ANN during 1960s to 1980s

Some key developments of this era are as follows

• 1961 Rosenblatt made an unsuccessful attempt but proposed the backpropagation scheme for multilayer networks.
• 1964 Taylor constructed a winner-take-all circuit with inhibitions among output units.
• 1969 Multilayer perceptron MLP MLP was invented by Minsky and Papert.
• 1971 Kohonen developed Associative memories.
• 1976 Stephen Grossberg and Gail Carpenter developed Adaptive resonance theory.

ANN from 1980s till Present

Some key developments of this era are as follows

• 1982 The major development was Hopfields Energy approach.
• 1985 Boltzmann machine was developed by Ackley, Hinton, and Sejnowski.
• 1986 Rumelhart, Hinton, and Williams introduced Generalised Delta Rule.
• 1988 Kosko developed Binary Associative Memory BAM BAM and also gave the concept of Fuzzy Logic in ANN.

The historical review shows that significant progress has been made in this field. Neural network based chips are emerging and applications to complex problems are being developed. Surely, today is a period of transition for neural network technology.

Biological Neuron

A nerve cell neuron neuron is a special biological cell that processes information. According to an estimation, there are huge number of neurons, approximately 10 with numerous interconnections, approximately 10 .

Schematic Diagram

Working of a Biological Neuron

As shown in the above diagram, a typical neuron consists of the following four parts with the help of which we can explain its working

• Dendrites They are tree-like branches, responsible for receiving the information from other neurons it is connected to. In other sense, we can say that they are like the ears of neuron.
• Soma It is the cell body of the neuron and is responsible for processing of information, they have received from dendrites.
• Axon It is just like a cable through which neurons send the information.
• Synapses It is the connection between the axon and other neuron dendrites.

ANN versus BNN

Before taking a look at the differences between Artificial Neural Network ANN ANN and Biological Neural Network BNN BNN, let us take a look at the similarities based on the terminology between these two.

Biological Neural Network BNN BNN	Artificial Neural Network ANN ANN
Soma	Node
Dendrites	Input
Synapse	Weights or Interconnections
Axon	Output

The following table shows the comparison between ANN and BNN based on some criteria mentioned.

Criteria	BNN	ANN
Processing	Massively parallel, slow but superior than ANN	Massively parallel, fast but inferior than BNN
Size	10 neurons and 10 interconnections	10 to 10 nodes mainlydependsonthetypeofapplicationandnetworkdesigner mainlydependsonthetypeofapplicationandnetworkdesigner
Learning	They can tolerate ambiguity	Very precise, structured and formatted data is required to tolerate ambiguity
Fault tolerance	Performance degrades with even partial damage	It is capable of robust performance, hence has the potential to be fault tolerant
Storage capacity	Stores the information in the synapse	Stores the information in continuous memory locations

Model of Artificial Neural Network

The following diagram represents the general model of ANN followed by its processing.

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