APPLICATION OF MULTI-SENSOR DATA FUSION BASED ON FUZZY NEURAL NETWORK IN ROTA TING MECHANICAL FAILURE DIAGNOSIS

At present, multi-se nsor fusion is widely used in object recognition and classification, since this technique can efficiently improve the accuracy and the ability of fault toleranc e. This paper describes a multi-sensor fusion system, which is model-based and used for rotating mechanical failure diagnosis. In the data fusion process, the fuzzy neural network is selected and used for the data fusion at report level. By comparing the experimental results of fault diagnoses based on fusion data wi th that on original separate data,it is shown that the former is more accurate than the latter.

INDUCTION MOTOR SPEED CONTROL SYSTEM BASED ON FUZZY NEURAL NETWORK

A fuzzy neural network controller with the teaching controller guidance and parameter regulations for vector-controlled induction motor is proposed. The design procedures of the fuzzy neural controller and the teaching controller are described. The parameters of the membership function are regulated by an on-line learning algorithm. The speed responses of the system under the condition, where the target functions are chosen as I qs and ω, are analyzed. The system responses with the variant of parameter moment of inertial J, viscous coefficients B and torque constant K tare also analyzed. Simulation results show that the control scheme and the controller have the advantages of rapid speed response and good robustness.

AN INTELLIGENT TOOL CONDITION MONITORING SYSTEM USING FUZZY NEURAL NETWORKS

Reliable on line cutting tool conditioning monitoring is an essential feature of automatic machine tool and flexible manufacturing system (FMS) and computer integrated manufacturing system (CIMS). Recently artificial neural networks (ANNs) are used for this purpose in conjunction with suitable sensory systems. The present work in Norwegian University of Science and Technology (NTNU) uses back propagation neural networks (BP) and fuzzy neural networks (FNN) to process the cutting tool state data measured with force and acoustic emission (AE) sensors, and implements a valuable on line tool condition monitoring system using the ANNs. Different ANN structures are designed and investigated to estimate the tool wear state based on the fusion of acoustic emission and force signals. Finally, four case studies are introduced for the sensing and ANN processing of the tool wear states and the failures of the tool with practical experiment examples. The results indicate that a tool wear identification system can be achieved using the sensors integration with ANNs, and that ANNs provide a very effective method of implementing sensor integration for on line monitoring of tool wear states and abnormalities.

Improving Land Resource Evaluation Using Fuzzy Neural Network Ensembles

Land evaluation factors often contain continuous-, discrete- and nominal-valued attributes. In traditional land evaluation, these different attributes are usually graded into categorical indexes by land resource experts, and the evaluation results rely heavily on experts＇ experiences. In order to overcome the shortcoming, we presented a fuzzy neural network ensemble method that did not require grading the evaluation factors into categorical indexes and could evaluate land resources by using the three kinds of attribute values directly. A fuzzy back propagation neural network （BPNN）, a fuzzy radial basis function neural network （RBFNN）, a fuzzy BPNN ensemble, and a fuzzy RBFNN ensemble were used to evaluate the land resources in Guangdong Province. The evaluation results by using the fuzzy BPNN ensemble and the fuzzy RBFNN ensemble were much better than those by using the single fuzzy BPNN and the single fuzzy RBFNN, and the error rate of the single fuzzy RBFNN or fuzzy RBFNN ensemble was lower than that of the single fuzzy BPNN or fuzzy BPNN ensemble, respectively. By using the fuzzy neural network ensembles, the validity of land resource evaluation was improved and reliance on land evaluators＇ experiences was considerably reduced.

Fuzzy Neural Network Model of 4-CBA Concentration for Industrial Purified Terephthalic Acid Oxidation Process

A fuzzy neural network (FNN) model is developed to predict the 4-CBA concentration of the oxidation unit in purified terephthalic acid process. Several technologies are used to deal with the process data before modeling.First,a set of preliminary input variables is selected according to prior knowledge and experience. Secondly,a method based on the maximum correlation coefficient is proposed to detect the dead time between the process variables and response variables. Finally, the fuzzy curve method is used to reduce the unimportant input variables.The simulation results based on industrial data show that the relative error range of the FNN model is narrower than that of the American Oil Company (AMOCO) model. Furthermore, the FNN model can predict the trend of the 4-CBA concentration more accurately.

Modeling of Energy Consumption and Effluent Quality Using Density Peaks-based Adaptive Fuzzy Neural Network

Modeling of energy consumption(EC) and effluent quality(EQ) are very essential problems that need to be solved for the multiobjective optimal control in the wastewater treatment process(WWTP). To address this issue, a density peaks-based adaptive fuzzy neural network(DP-AFNN) is proposed in this study. To obtain suitable fuzzy rules, a DP-based clustering method is applied to fit the cluster centers to process nonlinearity.The parameters of the extracted fuzzy rules are fine-tuned based on the improved Levenberg-Marquardt algorithm during the training process. Furthermore, the analysis of convergence is performed to guarantee the successful application of the DPAFNN. Finally, the proposed DP-AFNN is utilized to develop the models of EC and EQ in the WWTP. The experimental results show that the proposed DP-AFNN can achieve fast convergence speed and high prediction accuracy in comparison with some existing methods.

Modeling of grain size in isothermal compression of Ti-6Al-4V alloy using fuzzy neural network

Isothermal compression of Ti-6Al-4V alloy was conducted in the deformation temperature range of 1093-1303 K, the strain rates of 0.001, 0.01, 0.1, 1.0, and 10.0 s-1, and the height reductions of 20%-60% with an interval of 10%. After compression, the effect of the processing parameters including deformation temperature, strain rate, and height reduction on the flow stress and the microstructure was investigated. The grain size of primary a phase was measured using an OLYMPUS PMG3 microscope with the quantitative metallography SISC IAS V8.0 image analysis software. A model of grain size in isothermal compression of Ti-6A1-4V alloy was developed using fuzzy neural net- work （FNN） with back-propagation （BP） learning algorithm. The maximum difference and the average difference between the predicted and the experimental grain sizes of primary a phase are 13.31% and 7.62% for the sampled data, and 16.48% and 6.97% for the non-sampled data, respectively. It can be concluded that the present model with high prediction precision can be used to predict the grain size in isothermal compression of Ti-6Al-4V alloy.

A Short-Term Climate Prediction Model Based on a Modular Fuzzy Neural Network

In terms of the modular fuzzy neural network (MFNN) combining fuzzy c-mean (FCM) cluster and single-layer neural network, a short-term climate prediction model is developed. It is found from modeling results that the MFNN model for short-term climate prediction has advantages of simple structure, no hidden layer and stable network parameters because of the assembling of sound functions of the self-adaptive learning, association and fuzzy information processing of fuzzy mathematics and neural network methods. The case computational results of Guangxi flood season (JJA) rainfall show that the mean absolute error (MAE) and mean relative error (MRE) of the prediction during 1998-2002 are 68.8 mm and 9.78%, and in comparison with the regression method, under the conditions of the same predictors and period they are 97.8 mm and 12.28% respectively. Furthermore, it is also found from the stability analysis of the modular model that the change of the prediction results of independent samples with training times in the stably convergent interval of the model is less than 1.3 mm. The obvious oscillation phenomenon of prediction results with training times, such as in the common back-propagation neural network (BPNN) model, does not occur, indicating a better practical application potential of the MFNN model.

Fuzzy neural network control of underwater vehicles based on desired state programming

Due to the nonlinearity and uncertainty, the precise control of underwater vehicles in some intelligent operations hasn’t been solved very well yet. A novel method of control based on desired state programming was presented, which used the technique of fuzzy neural network. The structure of fuzzy neural network was constructed according to the moving characters and the back propagation algorithm was deduced. Simulation experiments were conducted on general detection remotely operated vehicle. The results show that there is a great improvement in response and precision over traditional control, and good robustness to the model’s uncertainty and external disturbance, which has theoretical and practical value.

Research on Prediction of Red Tide Based on Fuzzy Neural Network

In this paper, a four-layer fuzzy neural network using the Back Propagation （BP） Algorithm and the fuzzy logic was built to study the nonlinear relationships between different physical -chemical factors and the denseness of red tide algae, and to anticipate the denseness of the red tide algae. For the first time, the fuzzy neural network technology was applied to research the prediction of red tide. Compared with BP network and RBF network, the outcome of this method is better.

FAULT DIAGNOSIS OF ROTATING MACHINERY USING KNOWLEDGE-BASED FUZZY NEURAL NETWORK

A novel knowledge-based fuzzy neural network （KBFNN） for fault diagnosis is presented. Crude rules were extracted and the corresponding dependent factors and antecedent coverage factors were calculated firstly from the diagnostic sample based on rough sets theory. Then the number of rules was used to construct partially the structure of a fuzzy neural network and those factors were implemented as initial weights, with fuzzy output parameters being optimized by genetic algorithm. Such fuzzy neural network was called KBFNN. This KBFNN was utilized to identify typical faults of rotating machinery. Diagnostic results show that it has those merits of shorter training time and higher right diagnostic level compared to general fuzzy neural networks.

FUZZY NEURAL NETWORK FOR MACHINE PARTS RECOGNITION SYSTEM

The primary purpose is to develop a robust adaptive machine parts recognitionsystem. A fuzzy neural network classifier is proposed for machine parts classifier. It is anefficient modeling method. Through learning, it can approach a random nonlinear function. A fuzzyneural network classifier is presented based on fuzzy mapping model. It is used for machine partsclassification. The experimental system of machine parts classification is introduced. A robustleast square back-propagation (RLSBP) training algorithm which combines robust least square (RLS)with back-propagation (BP) algorithm is put forward. Simulation and experimental results show thatthe learning property of RLSBP is superior to BP.

APPROXIMATION CAPABILITIES OF MULTILAYER FEEDFORWARD REGULAR FUZZY NEURAL NETWORKS

Four layer feedforward regular fuzzy neural networks are constructed. Universal approximations to some continuous fuzzy functions defined on F 0 (R) n by the four layer fuzzy neural networks are shown. At first,multivariate Bernstein polynomials associated with fuzzy valued functions are empolyed to approximate continuous fuzzy valued functions defined on each compact set of R n . Secondly,by introducing cut preserving fuzzy mapping,the equivalent conditions for continuous fuzzy functions that can be arbitrarily closely approximated by regular fuzzy neural networks are shown. Finally a few of sufficient and necessary conditions for characterizing approximation capabilities of regular fuzzy neural networks are obtained. And some concrete fuzzy functions demonstrate our conclusions.

Adaptive Backstepping Output Feedback Control for SISO Nonlinear System Using Fuzzy Neural Networks

In this paper, a new fuzzy-neural adaptive control approach is developed for a class of single-input and single-output （SISO） nonlinear systems with unmeasured states. Using fuzzy neural networks to approximate the unknown nonlinear functions, a fuzzy- neural adaptive observer is introduced for state estimation as well as system identification. Under the framework of the backstepping design, fuzzy-neural adaptive output feedback control is constructed recursively. It is proven that the proposed fuzzy adaptive control approach guarantees the global boundedness property for all the signals, driving the tracking error to a small neighbordhood of the origin. Simulation example is included to illustrate the effectiveness of the proposed approach.

Fuzzy neural and chaotic searching hybrid algorithm and its application in electric customers’s credit risk evaluation

To evaluate the credit risk of customers in power market precisely, the new chaotic searching and fuzzy neural network (FNN) hybrid algorithm were proposed. By combining with the chaotic searching, the learning ability of the FNN was markedly enhanced. Customers’ actual credit flaw data of power supply enterprises were collected to carry on the real evaluation, which can be treated as example for the model. The result shows that the proposed method surpasses the traditional statistical models in regard to the precision of forecasting and has a practical value. Compared with the results of ordinary FNN and ANN, the precision of the proposed algorithm can be enhanced by 2.2% and 4.5%, respectively.

Transient Air-Fuel Ratio Control in a CNG Engine Using Fuzzy Neural Networks

The fuzzy neural networks has been used as means of precisely controlling the air-fuel ratio of a lean-burn compressed natural gas (CNG) engine. A control algorithm, without based on engine model, has been (utilized) to construct a feedforward/feedback control scheme to regulate the air-fuel ratio. Using fuzzy neural networks, a fuzzy neural hybrid controller is obtained based on PI controller. The new controller, which can adjust parameters online, has been tested in transient air-fuel ratio control of a CNG engine.

Using fuzzy neural networks for RMB/USD real exchange rate forecasting

In order to aim at improving the forecasting performance of the RMB/USD exchange rate, this paper proposes a new architecture of fuzzy neural networks based on fuzzy logic, and the method of point differential, which guarantees not only the direction of weight correction, but also the needed precision for the BP algorithm. In applying genetic algorithms for optimal performance, this approach, in the forecasting of the RMB/USD real exchange rate from 1994 to 2000, obviously outperforms typical BP Neural Networks and exhibits a higher capacity in regard to nonlinear, time-variablility, and illegibility of the exchange rate.

Performance analysis and fuzzy neural networks modeling of direct methanol fuel cell

This paper introduces the effects of cell operating temperature, methanol concentration and airflow rate, respectively, on the performance of direct methanol fuel cell （DMFC）. A novel method based on fuzzy neural networks identification technique is proposed to establish the performance model of DMFC. Three dynamic performance models of DMFC under the influences of cell operating temperature, methanol concentration, and airflow rate are identified and established separately. Simulation results show that modeling using fuzzy neural networks identification is satisfactory with high accuracy. It is applicable to DMFC control systems.

The Fuzzy Neural Network Control Scheme With H∞ Tracking Characteristic of Space Robot System With Dual-arm After Capturing a Spin Spacecraft

In this paper,the dynamic evolution for a dualarm space robot capturing a spacecraft is studied,the impact effect and the coordinated stabilization control problem for postimpact closed chain system are discussed.At first,the pre-impact dynamic equations of open chain dual-arm space robot are established by Lagrangian approach,and the dynamic equations of a spacecraft are obtained by Newton-Euler method.Based on the results,with the process of integral and simplify,the response of the dual-arm space robot impacted by the spacecraft is analyzed by momentum conservation law and force transfer law.The closed chain system is formed in the post-impact phase.Closed chain constraint equations are obtained by the constraints of closed-loop geometry and kinematics.With the closed chain constraint equations,the composite system dynamic equations are derived.Secondly,the recurrent fuzzy neural network control scheme is designed for calm motion of unstable closed chain system with uncertain system parameter.In order to overcome the effects of uncertain system inertial parameters,the recurrent fuzzy neural network is used to approximate the unknown part,the control method with H∞tracking characteristic.According to the Lyapunov theory,the global stability is demonstrated.Meanwhile,the weighted minimum-norm theory is introduced to distribute torques guarantee that cooperative operation between manipulators.At last,numerical examples simulate the response of the collision,and the efficiency of the control scheme is verified by the simulation results.

Rough Set Based Fuzzy Neural Network for Pattern Classification

A rough set based fuzzy neural network algorithm is proposed to solve the problem of pattern recognition. The least square algorithm (LSA) is used in the learning process of fuzzy neural network to obtain the performance of global convergence. In addition, the numbers of rules and the initial weights and structure of fuzzy neural networks are difficult to determine. Here rough sets are introduced to decide the numbers of rules and original weights. Finally, experiment results show the algorithm may get better effect than the BP algorithm.