30-11-2012, 04:03 PM
artificial neural network
. INTRODUCTION
This seminar is about the artificial neural network application in processing industry. An artificial neural network as a computing system is made up of a number of simple and highly interconnected processing elements, which processes information by its dynamic state response to external inputs. In recent times study of ANN models have gained rapid and increasing importance because of their potential to offer solutions to some of the problems in the area of computer science and artificial intelligence. Instead of performing a program of instructions sequentially, neural network models explore many competing hypothesis simultaneously using parallel nets composed of many computational elements. No assumptions will be made because no functional relationship will be established. Computational elements in neural networks are non linear models and also faster. Hence the result comes out through non linearity due to which the result is very accurate than other methods. The details of deferent neural networks and their learning algorithm are presented its clearly illustrator how multi layer neural network identifies the system using forward and inverse modeling approach and generates control signal. The method presented here are directed inverse, direct adaptive, internal model and direct model reference control based ANN techniques.
2. NEURAL NETWORKS
Artificial neural networks have emerged from the studies of how brain performs. The human brain consists of many million of individual processing elements, called neurons that are highly interconnected.
Information from the outputs of the neurons, in the form of electric pulses is received by the cells at connections called synapses. The synapses connect to the cell inputs, or dendrites and the single output pf the neuron appears at the axon. An electric pulse is sent down the axon when the total input stimuli fro all of the dendrites exceed a certain threshold.
Artificial neural networks are made up of simplified individual models of the biological neuron that are connected together to form a network. Information is stored in the network in the form of weights or different connections strengths associated with synapses in the artificial neuron models.
Many different types of neural networks are available and multi layer neural networks are the most popular which are extremely successful in pattern reorganization problems. An artificial neuron model is shown below. Each neuron input is weighted by W. changing the weights of an element will alter the behavior of the whole network. The output y is obtained by summing the weighted inputs to the neuron and passing the result through a non-linear activation function, f ().
Multi layer networks consists of an input layer, a hidden layer are made up of no. of nodes. Data flows through the network in one direction only, from input to output; hence this type of network is called a feed-forwarded network. A two-layered network is shown below.
2.1 NEURAL NETWORKS IN PROCESS CONTROL
Artificial neural networks are implemented as software packages in computers and being used to incorporate of artificial intelligence in control system. ANN is basically mathematical tools which are being designed to employ principles similar to neurons networks of biological system. ANN is able to emulate the information processing capabilities of biological neural system. ANN has overcome many of the difficulties that t conventional adaptive control systems suffer while dealing with non linear behavior of process.
2.2 PROCEDURES FOR ANN SYSTEM ENGINEERING
In realistic application the design of ANN system is complex, usually iterative and interactive task. Although it is impossible to provide an all inclusive algorithmic procedure, the following highly interrelated, skeletal steps reflect typical efforts and concerns. The plethora of possible ANN design parameters include:
The interconnection strategy/network topology/network structure.
Unit characteristics (may vary within the network and within subdivisions within the network such as layers).
Training procedures.
Training and test sets.
Input/output representation and pre- and post-processing.
2.3 FEATURES OF ANN
Their ability to represent nonlinear relations makes them well suited for non linear modeling in control systems.
Adaptation and learning in uncertain system through off line and on line weight adaptation
Parallel processing architecture allows fast processing for large-scale dynamic system.
Neural network can handle large number of inputs and can have many outputs.
Neural network architecture have learning algorithm associated with them. The most popular network architecture used for control purpose is multi layered neural network [MLNN] with error propagation [EBP] algorithm.