Application of Neural Network in Letter Recognition Using the Perceptron Method

Modeling with neural networks is the learning and adjustment of an object. The perceptron method is a learning method with supervision in a neural network system. In designing a neural network that needs to be considered is the number of specifications that will be identified. A neural network consists of a number of neurons and a number of inputs. To identify some letters, it takes several neurons to distinguish them. These neurons will generate a combination value that is used to identify the letters. so that the resulting network must have parameters that can be set by changing through the rules of learning with supervision.

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The Health Monitoring of Bridges using Artificial Neural Networks

Journal of Mechanics ◽

10.1017/s1727719100004524 ◽

2001 ◽

Vol 17 (3) ◽

pp. 157-166

Author(s):

Pei-Ling Liu ◽

Shyh-Jang Sun

Keyword(s):

Neural Network ◽

Neural Networks ◽

Monitoring System ◽

Health Monitoring ◽

Network System ◽

Bridge Structure ◽

Constant Mass ◽

Simulation Data ◽

Neural Network System ◽

The Neural Networks

ABSTRACTThis study develops a neural network system to monitor the safety of a bridge structure. A truck of constant mass is driven at constant speed through the target bridge. Then, the maximal and minimal values of the bridge elongations are processed by a monitoring system to evaluate the current condition of the bridge. The monitoring system is composed of parallel backpropagation neural networks. Each neural network monitors a part of the bridge. The neural networks are trained using simulation data. The numerical example shows that the monitoring system is effective in the damage detection of the bridge.

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Gas Turbine and Sensor Fault Diagnosis With Nested Artificial Neural Networks

Volume 7: Turbo Expo 2004 ◽

10.1115/gt2004-53570 ◽

2004 ◽

Cited By ~ 1

Author(s):

N. Xiradakis ◽

Y. G. Li

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Networks ◽

Gas Turbine ◽

Diagnostic System ◽

Network System ◽

Fault Recovery ◽

Sensor Fault ◽

Engine Component ◽

Neural Network System

Accurate gas turbine diagnosis relies on accurate measurements from sensors. Unfortunately, sensors are prone to degradation or failure during gas turbine operations. In this paper a stack of decentralised artificial neural networks are introduced and investigated as an approach to approximate the measurement of a failed sensor once it is detected. Such a system is embedded into a nested neural network system for gas turbine diagnosis. The whole neural network diagnostic system consists of a number of feedforward neural networks for engine component diagnosis, sensor fault detection and isolation; and a stack of decentralised neural networks for sensor fault recovery. The application of the decentralised neural networks for the recovery of any failed sensor has the advantage that the configuration of the nested neural network system for engine component diagnosis is relatively simple as the system does not take into account sensor failure. When a sensor fails, the biased measurement of the failed sensor is replaced with a recovered measurement approximated with the measurements of other healthy sensors. The developed approach has been applied to an engine similar to the industrial 2-shaft engine, GE LM2500+, whose performance and training samples are simulated with an aero-thermodynamic modelling tool — Cranfield University’s TURBOMATCH computer program. Analysis shows that the use of the stack of decentralised neural networks for sensor fault recovery can effectively recover the measurement of a failed sensor. Comparison between the performance of the diagnostic system with and without the decentralised neural networks shows that the sensor recovery can improve the performance of the neural network engine diagnostic system significantly when a sensor fault is present.

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Neural Network System Design for Predicting MIN Reliability

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.g5732.059720 ◽

2020 ◽

Vol 9 (7) ◽

pp. 1216-1221

Keyword(s):

Neural Network ◽

Neural Networks ◽

System Design ◽

Interconnection Networks ◽

Network System ◽

Multistage Interconnection Networks ◽

Number Of Layers ◽

Neural Network System ◽

Improved Performance ◽

Number Of Iterations

fforts have been made to examine and study different path and multi-path Multistage Interconnection Networks (MIN) possessing regular or irregular topology. Numerous strategies for establishing fault-tolerance in MINs have also been studied. These studies have provided us help to understand the strength and weakness of the existing static and dynamic and regular and irregular MINs. Application of Neural Networks leads to the development of MINs with improved performance and study of its Reliability In this paper ANN based system has been developed which will help in the study of metrics required for enhancing and predicting the reliability of MINs. In this paper Number of iterations are conducted to improve the ANN based system to predict the reliability of MINs by changing the number of neurons and the number of layers.

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Discussion forH-Matrices and It’s Application

Journal of Applied Mathematics ◽

10.1155/2014/870592 ◽

2014 ◽

Vol 2014 ◽

pp. 1-6

Author(s):

Guichun Han ◽

Huishuang Gao ◽

Haitao Yang

Keyword(s):

Neural Network ◽

Neural Networks ◽

Network System ◽

Numerical Examples ◽

Diagonally Dominant Matrices ◽

Neural Network System ◽

Diagonally Dominant ◽

The Stability

NonsingularH-matrices and positive stable matrices play an important role in the stability of neural network system. In this paper, some criteria for nonsingularH-matrices are obtained by the theory of diagonally dominant matrices and the obtained result is introduced into identifying the stability of neural networks. So the criteria for nonsingularH-matrices are expanded and their application on neural network system is given. Finally, the effectiveness of the results is illustrated by numerical examples.

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Optimum Design of Structures for Earthquake Loading by a Cellular Evolutionary Algorithm and Neural Networks

Structural Seismic Design Optimization and Earthquake Engineering ◽

10.4018/978-1-4666-1640-0.ch012 ◽

2012 ◽

pp. 306-322 ◽

Cited By ~ 1

Author(s):

Saeed Gholizadeh

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Neural Networks ◽

Optimum Design ◽

Optimization Algorithm ◽

Time History ◽

Network System ◽

Hybrid Neural Network ◽

Neural Network System ◽

Nonlinear Time History

The present chapter deals with optimum design of structures for earthquake induced loads by taking into account nonlinear time history structural response. As the structural seismic optimization is a time consuming and computationally intensive task, in this chapter, a methodology is proposed to reduce the computational burden. The proposed methodology consists of an efficient optimization algorithm and a hybrid neural network system to effectively predict the nonlinear time history responses of structures. The employed optimization algorithm is a modified cellular genetic algorithm which reduces the required generation numbers compared with the standard genetic algorithm. Also, the hybrid neural network system is a combination of probabilistic and generalized regression neural networks. Numerical results demonstrate the computational merits of the proposed methodology for seismic design optimization of structures.

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Hierarchical Neural Networks for Modelling Adaptive Financial Systems

Artificial Neural Networks in Finance and Manufacturing ◽

10.4018/978-1-59140-670-9.ch006 ◽

2006 ◽

pp. 109-123

Author(s):

Masoud Mohammadian ◽

Mark Kingham

Keyword(s):

Neural Network ◽

Neural Networks ◽

Interest Rate ◽

Interest Rates ◽

Network System ◽

Financial Systems ◽

Neural Network System ◽

Hierarchical Neural Network ◽

Hierarchical Neural Networks ◽

Accuracy Of Prediction

In this chapter, an intelligent hierarchical neural network system for prediction and modelling of interest rates in Australia is developed. A hierarchical neural network system is developed to model and predict 3 months’ (quarterly) interest-rate fluctuations. The system is further trained to model and predict interest rates for 6-month and 1-year periods. The proposed system is developed with first four and then five hierarchical neural networks to model and predict interest rates. Conclusions on the accuracy of prediction using hierarchical neural networks are also reported.

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