Abstract: It is available in the file which you can download from the link below.

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Abstract: Neural networks deals with the development of intelligent systems which recognize things based on some knowledge base. The recognition of optical characters is known to be one of the earliest applications of Artificial Neural Networks, which partially emulate human thinking in the domain of artificial intelligence. This paper emphasizes on a simplified neural approach to recognition of optical or visual characters .

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Abstract:

This paper focuses on the bidirectional associative memory its features and the future aspects and the current context of application BAM is a type of neural network. Artificial neural network (Ann’s) resembled the human nervous system, with algorithms consisting of weighted interconnecting processing units (like neural map of the human brain).

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Abstract:

An NN, in general, is a highly interconnected network of a large number of processing elements called neurons in an architecture inspired by the brain, an NN can be massively parallel and therefore is said to exhibit parallel distributed process.

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Abstract:

All missile guidance systems require target state information in order to achieve the target. The accuracy of the modern guidance schemes critically depend on the accuracy of the target state estimates

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Abstract:This paper presents a method for short term load forecasting in eectric power system using artificial neural network. A multilayered feed forward network with back propagation learning algorithm is used because its good generalising property It is available in the file which you can download from the link below.

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Abstract:A neural network is a machine that is designed to model the way in which the brain performs a particular task or function of interest. A neural network or artificial neural network (ANN) is an interconnected group of artificial neurons that uses a mathematical model or computational model for information processing based on a connectionist approach to computation It is available in the file which you can download from the link below.

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Abstract: The detection of rail defects is a labor-intensive process in spite of recent advances made with modern rail detection equipment. Such detection systems provide facilities for both the collection and analysis of rail data. While these systems are effective, it is desirable to improve their analysis capabilities in order to both expedite the rail detection process and to increase the accuracy of the rail detection process. The objective of this research investigation was to determine the feasibility of using neural networks to improve the automated detection and classification of rail defects. Since the process of recognizing defective rail depends primarily on the ability to identify irregular patterns in the data and since neural networks are well-known for their ability to detect patterns in data [2, 4, 5, 6], the application of neural networks to this problem was natural.

The basic approach consisted of performing neural network analysis on actual rail data. Union Pacific Railroad (UPRR) rail detector crews collected this data from several different locations. The data was collected and analyzed using UPRR's System 1000 rail detection system from Harsco Track Technologies. The rail detector crews monitored the collection and analysis and performed hand testing as needed. After studying the complexity of the data, it was decided that this pilot study would focus on the identification of defective bolt holes.

The results obtained strongly indicate that neural networks can provide an effective tool for enhancing both the speed and accuracy of the automated identification of defective bolt holes. From investigating transducer outputs obtained from various types of rail defects, it appears that it would not be difficult to extend this approach to other types of rail defects.


Courtesy: N.Santhanam, Adi Parasakthi Engineering College (APEC), Melmaruvathur, Tamilnadu

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Abstract:Contingency analysis of a power system is a major activity in power system planning and operation. In general an outage of one transmission line or transformer may lead to over loads in other branches and/or sudden system voltage rise or drop. It is available in the file which you can download from the link below.

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Abstract: The electric power industry is currently undergoing an unprecedented reform, ascribable to, one of the most exciting and potentially profitable recent developments in increasing usage of artificial intelligence techniques. The artificial neural network approach has attracted number of applications especially in the field of power system since it is a model free estimator. Neural networks provide solutions to very complex and nonlinear problems. Nonlinear problems, like load forecasting that cannot be solved with standard algorithms but can be solved with a neural network with remarkable accuracy. Modern interconnected power systems often consist of thousands of pieces of equipment each of which may have an effect on the security of the system. Neural networks have shown great promise for their ability to quickly and accurately predict the system security when trained with data collected from a small subset of system variables.

The intention of this paper is to give an overview of application of artificial intelligence and neural network (NN) techniques in power systems to prognosticate load on power plant and contingency in case of any unexpected outage. In this paper we present the key concepts of artificial neural networks, its history, imitation of brain neuron’s architecture and finally the applications (load forecasting and contingency analysis). The applications of artificial intelligence in areas of load forecasting by error Backpropagation learning algorithm and contingency analysis based on Quality index have been perspicuously explained

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