State Forecasting for Rotary Machine Based on Neural Network and Genetic Algorithm

2007 ◽  
Author(s):  
Hongmei Liu ◽  
Shaoping Wang ◽  
Pingchao Ouyang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
The State ◽  
Structure Parameters ◽  
Hydraulic Pump ◽  
Bp Network ◽  
New Approach ◽  
Rotary Machine ◽  
Optimal Network ◽  
Forecasting Method

A state forecasting is a key technology to achieve the advanced predictive maintenance. A Prediction based on neural network is a new approach to realize the state predicting. The present neural networks predicting models are comparatively poor in adaptability to environment and in predicting accuracy, therefore, a new rotary machine online state forecasting method based on the genetic algorithm (GA) and neural network (NN) was presented. GA was used for dynamical optimizing the structure parameters of BP network to obtain the optimal network structure. A training algorithm combining GA with BP was adopted to avoid the local minimum and to heighten the learning precision. The state predicting results for hydraulic pump indicate that the predicting model purposed may dynamically optimize the structure parameters in accordance with different conditions, and gained satisfactory results.

Transformer Fault Diagnosis Based on Improved Quantum Genetic Algorithm and BP Network

2010 ◽  
Vol 29-32 ◽  
pp. 1543-1549 ◽  
Author(s):  
Jie Wei ◽  
Hong Yu ◽  
Jin Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Fuzzy Theory ◽  
Back Propagation ◽  
Gas Analysis ◽  
Bp Network ◽  
Quantum Genetic Algorithm ◽  
Transformer Fault

Three-ratio of the IEC is a convenient and effective approach for transformer fault diagnosis in the dissolved gas analysis (DGA). Fuzzy theory is used to preprocess the three-ratio for its boundary that is too absolute. As the same time, an improved quantum genetic algorithm IQGA (QGASAC) is used to optimize the weight and threshold of the back propagation (BP). The local and global searching ability of the QGASAC approach is utilized to find the BP optimization solution. It can overcome the slower convergence velocity and hardly getting the optimization of the BP neural network. So, aiming at the shortcoming of BP neural network and three-ratio, blurring the boundary of the gas ratio and the QGASAC algorithm is introduced to optimize the BP network. Then the QGASAC-IECBP method is proposed in this paper. Experimental results indicate that the proposed algorithm in this paper that both convergence velocity and veracity are all improved to some extent. And in this paper, the proposed algorithm is robust and practical.

Power Information System Risk Assessment Method Based on Genetic Algorithms and Neural Network

2014 ◽  
Vol 530-531 ◽  
pp. 429-433 ◽  
Author(s):  
Heng Yang ◽  
Ru Sen Fan ◽  
Dong Hui Xu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Risk Assessment ◽  
Information System ◽  
Evaluation Method ◽  
Assessment Method ◽  
Local Extremum ◽  
Bp Algorithm ◽  
Bp Network ◽  
System Risk

In order to scientifically and accurately evaluate power information system, the new power information risk evaluation method based on the genetic algorithm and BP neural network is presented. The method combining the genetic algorithm and BP algorithm can be used to train the feedforward neural network , namely, first , to use the genetic algorithm to do the global training, then ,to use BP algorithm to do local precise training ,which not only overcomes the drawbacks of the traditional BP network (the training time is long, and the network is easy to fall to local extremum),but also improves the global convergence efficiency. The method was adopted to evaluate the power information system. And findings identify that the new method has distinctive convergence speed and high predicition accuracy, which provides a new concept for power information system risk assessment.

An Adaptive Second Order Neural Network with Genetic-Algorithm-based Training (ASONN-GA) to Forecast the Closing Prices of the Stock Market

2020 ◽  
pp. 238-256
Author(s):  
Sarat Chandra Nayak ◽  
Bijan Bihari Misra ◽  
Himansu Sekhar Behera
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Stock Market ◽  
Single Layer ◽  
Second Order ◽  
Optimal Network ◽  
Efficient Prediction ◽  
Higher Order Neural Networks ◽  
The One

Successful prediction of stock indices could yield significant profit and hence require an efficient prediction system. Higher order neural networks (HONN) have several advantages over traditional neural networks such as stronger approximation, higher fault tolerance capacity and faster convergence characteristics. This paper proposes an adaptive single layer second order neural network with genetic algorithm based training (ASONN-GA) applied to forecast daily closing prices of the stock market. For comparative study of performance, two conventional neural based models such as a recurrent neural network (RNN) and a multilayer perceptron (MLP) have been developed. The optimal network parameters for all the three models are tuned by genetic algorithm (GA). The efficiencies of the models have been evaluated by forecasting the one-day-ahead closing prices of real stock markets. From simulation studies, it is revealed that the ASONN-GA model achieve better forecasting accuracy over other two models.

scholarly journals Hybrid Ensemble Construction with Selected Neural Networks

2011 ◽  
Vol 15 (6) ◽  
pp. 652-661 ◽  
Author(s):  
M. A. H. Akhand ◽  
◽  
Pintu Chandra Shill ◽  
Kazuyuki Murase ◽  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Task Performance ◽  
Benchmark Problems ◽  
Neural Network Ensemble ◽  
Improve Performance ◽  
Negative Correlation Learning ◽  
Optimal Network ◽  
Correlation Learning ◽  
The Individual

A Neural Network Ensemble (NNE) is convenient for improving classification task performance. Among the remarkable number of methods based on different techniques for constructing NNEs, Negative Correlation Learning (NCL), bagging, and boosting are the most popular. None of them, however, could show better performance for all problems. To improve performance combining the complementary strengths of the individual methods, we propose two different ways to construct hybrid ensembles combining NCL with bagging and boosting. One produces a pool of predefined numbers of networks using standard NCL and bagging (or boosting) and then uses a genetic algorithm to select an optimal network subset for an NNE from the pool. Results of experiments confirmed that our proposals show consistently better performance with concise ensembles than conventional methods when tested using a suite of 25 benchmark problems.

scholarly journals Development of object state evaluation method in intelligent decision support systems

2021 ◽  
Vol 6 (9 (114)) ◽  
pp. 54-63
Author(s):  
Yurii Zhuravskyi ◽  
Oleg Sova ◽  
Serhii Korobchenko ◽  
Vitaliy Baginsky ◽  
Yurii Tsimura ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Methodological Approach ◽  
Cognitive Model ◽  
Cognitive Models ◽  
The State ◽  
Data Uncertainty ◽  
Training Procedure ◽  
Artificial Neural

Accurate and objective object analysis requires multi-parameter estimation with significant computational costs. A methodological approach to improve the accuracy of assessing the state of the monitored object is proposed. This methodological approach is based on a combination of fuzzy cognitive models, advanced genetic algorithm and evolving artificial neural networks. The methodological approach has the following sequence of actions: building a fuzzy cognitive model; correcting the fuzzy cognitive model and training knowledge bases. The distinctive features of the methodological approach are that the type of data uncertainty and noise is taken into account while constructing the state of the monitored object using fuzzy cognitive models. The novelties while correcting fuzzy cognitive models using a genetic algorithm are taking into account the type of data uncertainty, taking into account the adaptability of individuals to iteration, duration of the existence of individuals and topology of the fuzzy cognitive model. The advanced genetic algorithm increases the efficiency of correcting factors and the relationships between them in the fuzzy cognitive model. This is achieved by finding solutions in different directions by several individuals in the population. The training procedure consists in learning the synaptic weights of the artificial neural network, the type and parameters of the membership function and the architecture of individual elements and the architecture of the artificial neural network as a whole. The use of the method allows increasing the efficiency of data processing at the level of 16–24 % using additional advanced procedures. The proposed methodological approach should be used to solve the problems of assessing complex and dynamic processes characterized by a high degree of complexity.

Method of AC Servo System Based on Genetic Algorithm and Neural Network PID Control

2013 ◽  
Vol 717 ◽  
pp. 563-567 ◽  
Author(s):  
Wen Chun Chang ◽  
Cheng Chen
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Model ◽  
Network Structure ◽  
Bp Neural Network ◽  
Extreme Values ◽  
Learning Algorithm ◽  
Bp Algorithm ◽  
Bp Network ◽  
Neural Network Learning

BP network model has become one of the important neural network model, is used in many fields, but it has some defects. As from a mathematical perspective, it is a nonlinear optimization problem, which inevitably has the local minima problem; BP neural network learning algorithm has slow convergence rate, and the convergence speed and the initial weights of choice; network structure, namely the hidden layer nodes selection is still no theory until, but according to the experience. Based on the BP algorithm the local extreme values, considering the genetic algorithm and BP algorithm is combined with, on the BP neural network optimization. Neural network using genetic algorithm optimization mainly includes three aspects: the connection weights of evolution, evolutionary network structure, learning the rules of evolution.

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