Drift error compensation algorithm for fiber optic gyro based on T-S fuzzy modeling

A drift error nonlinear compensation algorithm for Fiber Optic Gyro (FOG) is presented based on T-S fuzzy model with the antecedent parameters identified by G-K clustering algorithm and the error model of T-S fuzzy model with the consequent parameters identified by least square algorithm. The computed results show that this model can compensate the original data effectively, while the error principles of FOG do not need to be understood well. Comparing with the original data, compensation with linear fitting and compensation with neural network, the absolute error of the proposed model reduces by 99%, 96% and 10%, respectively. The error variance reduces by 99%, 98% and 20%, respectively. The results indicate that this proposed algorithm can be simply operated with high precision and easy to realize in engineering.     

Cost forecast model for power engineering based on based on back propagation neural networks

A novel power engineering cost forecast model was proposed by combining feature extraction and small sample learning. The initial data was preprocessed with principal component analysis to remove the correlation among the original indexes and get the potential independent indexes. The new indexes acted as the input set to build a new forecast model based on least squares support vector machines. The results of this model were compared with the forecast results getting from artificial neural network. By comparing the forecast results with different principal components number, the optimal number was determined to achieve the desired forecast effect. The prediction results indicate that the method can extract the feature of initial data effectively and is good at small sample learning . The expected forecasting results can be reached.     

Water Quality Forecasting Method Based on Compensative Fuzzy Neural Network

The forecasting of water quality variation is very important in the process of sewage treatment, which helps the control system work reliably and steadily. In this paper, the compensative fuzzy neural network (CFNN) based on compensative fuzzy logic and neural network and its study arithmetic are introduced. Considering its features as fast speed, steady studying course, global dynamic optimization, CFNN is applied to establish water quality forecasting model. The practical example indicates that the model is not sensitive to initial parameters and has better forecasting precision and faster convergence.     

Application of data mining method in power load bad data intelligent identification and correction

There is a number of bad data in the load database produced, thus the data must be cleaned before it is used to forecasting electric load or performing power system analysis. The WKFCM measures distance by kernel functions instead of the complicated Euclidean distance and this kernel based distance is used as dissimilarity function of target clustering formula which can reduce the calculation complexity. After the clustering, a super circle covering neural network based identification model for load data is proposed, and the bad data is modified. It is proved that the proposed data processing model has good effect.     

analysis and application of demand forecasting model with multi element variable parameters

A forecasting model for logistics demand was presented to overcome the limitations of single goal forecasts of logistics demand and forecast data complexity. Based on the forecasting evaluation index and pretreatment of rough set theory, a multi input and multi output wavelet network (MMWNN) model for forecasting multi element regional logistics demand was studied. The network configuration was confirmed using the stepwise checkout and iterative gradient descent methods. After rough set reduction, the evaluation index was used to forecast the multi element regional logistics demand. The results of the numerical example indicate the feasibility and effectiveness of the model.     

Fuzzy inference in the inter-wire crosstalk prediction

This paper presents the application of fuzzy inference on the issue of predicting crosstalk between interconnection wires. Effective electromagnetic interference parameters are selected as predictors from the pre-survey data, and the data are classified according to their statistical properties, and then fuzzy implication sentences are merged to determine the relationship between fuzzy implication set. Compared with the artificial neural network forecasting method,fuzzy inference forecasting method can not only make full use of existing experience and knowledge of experts, but also extract antomation summary and the new inference rules. The result of the example shows that the method is feasible.     

Application of Incremental Genetic RBF NN in Converter Vanadium Recover

Converter vanadium recover is a very sophisticate reaction which is diverse and non- line. From the point of view of statistics and reaction mechanism, it is difficult to build up end- point control static model. Aim at this problem, the paper puts forward a model identify method based on incremental genetic RBF neural network to build up such a model, which can perfectly resolve the problem of random selection of RBF cluster center number and sample data clustering. Furthermore, in order to ensure structure of neural network to fit with continuous incremental data set, the paper presents a method of incremental data dealing, which is applied to amend the parameters of neural network. Then the request of continuous production is satisfied. Finally the result of test shows that after adopting the algorithm, the error of result is less than before and end- point hitting ratio satisfies to ninety percent. These indicate the algorithm has the engineering practicability.     

A new integrating forecast and its application in short-term climate prediction〖JP〗

The existing problems of the traditional weight integrating forecast methods and the application in climate prediction are analyzed. A new method based on data mining is presented, which uses BP artificial neural network to build the integrating forecast classifier to integrate the forecast results of sub-methods. According to the features of different forecast objects, this method can change weight dynamically, which overcomes the shortage of the traditional weight integrating forecasts that cannot change weight after been decided and overcomes the shortage that cannot get the optimal results. By predicting the precipitation and average temperature of Chengkou County in January, and spring drought index of Chongqing from 2001 to 2007, the experiment results show that the reliability and accuracy of the proposed model are better than those of the sub-methods and other integrating forecast methods, which proves the effectiveness of this method.     

Intrusion Detection Based on Data Mining

As computer networks play increasingly vital roles in modern society, information security becomes one of the most important research issues in the field of information technology. But intrusions cause a serious security risk, how to efficiently prevent and detect intrusions becomes one of hot research problems in the field of information supervision. The traditional process of building the model of intrusion detection is slow, whose cost of research and development is high. However, data mining has unique advantages in acquiring unknown knowledge. So, intrusion detection based on data mining becomes a hot issue. The research background, architectures, techniques, problems to be solved and the future direction are discussed after analyzing current status of network intrusion and situation of R&D on intrusion detection and data mining.     

Using BP Neural Network to Determine the Membership Function of the Input of Fuzzy Control System

While design the fuzzy controller, it is very important to determine the membership function of fuzzy variables.The data can be broadly classified as fuzzy sets by using the classification property of the BP neural network. The author selects a BP neural network with one hide layer and uses S function to the input and hide layer, and linear function to the output layer.Advanced BP algorithm isused to train the BP neural network in the environment of MATLAB . The nearer to the target values is the better the last output is.With the trained BP network , the membership values of the inputs can be got ten. This method has high rate and low error.     

The Multi-objective Fuzzy Optimal Power Flow Model and its Neural Network Algorithm

This paper has presented a multi-objective fuzzy optimal power flow medel.Inthe model , the multiple objectives, such as the minimum generation cost and the minimum powerloss, have been considered simultaneously, A new algorithm based on neural network models is aisopresented,in which the neural networks are employed to express, the membership function of fuzzysets and solve the optimization problems. The validity of model and algorithm is verified with numerical examples.     

Studies on Information Network of Quality Management in Building Engineering

With improvement of construction management and development of computer and network technologies, network technologies are being widely applied in building engineering. This paper discusses how to apply database technology and network technology to design and develop information network systems of building engineering and combine the advanced management methods with computer network technology to share the quality information in construction industry. The solution given by this paper is useful and can be extended to and used in other systems.     

THE FORECASTING MODEL AND ITS USE OF SCIENCE AND TECHNOLOGY DEVELOPMENT ZONES

The model for forecasting the development of the Science and Technology Development Zones(STDZ) and the High Technology Industry Developrhedt Zones (HTIDZ) is created with the help, of the forecasting theory and technology in system engineering, A scheme for handling the dynamic variables in the model is proposed. The development targets of the Chongqing Shapingba Science and Technology Development Zone in the coming six years are forecasted .by using the model. The model can be used widely to forecast the development of STDZ and HTIDZ and other similar social and economical systems.     

Application of Artificial Neural Network in Modeling Paint Waste Water Treated by Coagulation Oxidation Process

An artificial neural network (ANN) model was established based on data of paint waste water treated by coagulation oxidation process, using the improved back propagation algorithm. The model was then used to fit and predict some experimental data. The results indicated that the errors between computed data and experimental data were much small. Furthermore, the ANN model could correctly reflect the mechanism of some factors which affected the efficiency of paint waste water treatment.     

Intelligent Extracting Method for Feature Mode of Electric Days Load

Generally there have a number of bad data in the electric load data and it affects the precision of load forecasting,so it is necessary for extracting the feature mode of days load data,then cleaning the load data before it is used to forecasting electric load or performing power system analysis.Inspired by soft clustering thought,a intelligent feature mode of days load data extracting method is proposed based on the mutual offset of fuzzy c-means clustering arithmetic and Kohonen self organization feature map neural network.With the merits of not only high extracting precision and convergent speed but also dynamic calculation capability,the method proposed can supply load forecasting or system analysis procedure with due data.Test results using actual data of Chengqu power supply bureau in Chongqing demonstrate the effectivity and feasibility of the method.     

The Fuzzy Inputing And Outputing Method in Vibration Fault Diagnosis of Steam Turbine-Generator Set

A fuzzy neural network diagnosis model is established on the basis of the vibration failure features of steam turbine-gernerator set, two kinds of fuzzy inputing method are discussed. At last, the performance of the fuzzy neural network is compared with that of the conventional BP network. The results show that the method presented is suitable for identifying the vibration failure of steam turbine-gernerator set, and it is more efficient in deal with the uncertain data than BP network diagnosis.     

Pre-bid Decision-making Process Based on Artificial Neural Networks Technology

There are complicated non-linear relations among influence factors, and between influence factors and decision - making results, which cannot be handled by common analytical method effectively. So the ANN technology was applied in the pre-bid decision-making processes. An indicator system of influence factors for pre-bid decision-making process was built based on extensive investigations, comparisons and analyses, with due considerations to the ANN analysis requirements. And then the criterion and method for quantifying the indicators were put forward by quantificational methods and fuzzy theories. An ANN model for pre-bid decision- making process was constructed and performed in Matlab, which has powerful learning ability and fast learning speed. The model was verified by a few examples from engineering practice.     

a multi level fuzzy comprehensive evaluation analytical model of a regional manufacturing information engineering industry

Regional manufacturing information engineering industries are extremely large and complex systems. Based on the implemental integer effect and an evaluation requirement of regional manufacturing information engineering, we analyzed the objectives and characteristics of regional manufacturing information engineering, proposed a multiple evaluation index method of regional manufacturing information engineering. Based on a multiple integration evaluation method, we built a model to evaluate regional manufacturing information engineering. We proposed a measurement model for regional manufacturing information engineering based on multi level fuzzy comprehensive evaluation. Our quantitative research regarding the implementation effectiveness of regional manufacturing information engineering was based on fuzzy quantitative indicators. The model was grounded on collecting indexes of regional manufacturing industry information engineering, and used a fuzzy matrix to normalize the assessment results to a single fuzzy comprehensive evaluation level. Measurement was carried out step by step. Multi level comprehensive evaluation results for a regional manufacturing information engineering industry were obtained. The validity of the model was shown through application practice.     

Organically Combinatorial Inference and Optimization in Intelligence System

The inference algorithm is the most important part in intelligence system because the level of intelligence in the system is decided by it. By means of the mutual benefit for inference algorithm of expert system ,fuzzy logic and neural network ,the combinatorial inference technology which is organically composed of these three parts is put forward for inference mechanism in intelligence system. The optimization decision model is also set up. In order to bring all the advantage of every inference algorithm into play and overcome the disadvantage of single inference algorithm the common expert knowledge base is applied to organic combination and concurrent operation of all inference algorithm. In order to realize knowledge share the inference results are optimized by decision technology optimization. The research results show that the organized combinatorial inference and optimization can be applied in engineering practice effectively and is benefit for raising the inference level.