Fuzzy Radial Basis Function Neural Network Research Articles

Soft computing technique with maintenance and controlling for distributed energy using differential evolution based local power distribution system and fuzzy radial basis function neural network

Learning-based modelling methods are now being used to construct a precise prediction method for renewable energy sources. CI (Computational Intelligence) methods are proven to be effective in developing and optimising renewable instruments. Difficulty of this type of energy is determined by its coverage of enormous amounts of data as well as factors that must be properly analyzed. Soft computing approaches are widely recognized as critical instruments for improving performance of spinning electrical devices in both controls as well as design. The progress of gentle computing methods employed in rotating electrical machines is crucial for a wide range of energy conversion devices, including generators, high-performance electric engines and electric cars.This research proposes a novel technique for maintaining and controlling distributed energy systems. Here the maintenance and controlling data have been evaluated by differential evolution based local power distribution system (DE-LPDS) for control system as well as fuzzy radial basis function neural network (FRBFNN) is used in Micro-grid energy management system. The suggested energy management system is modelled to coordinate among many flexible sources by establishing priority resources, direct demand control signals, and power prices. Experimental results show various maintenance and controlling datasets in terms of QoS of 82%, energy efficiency of 95%, power consumption of 59 %, computational time of 34 ms and training accuracy of 95 % by proposed technique.

Thermal error analysis of tauren EDM machine tool based on FCM fuzzy clustering and RBF neural network

A method that combines temperature field detection, adaptive FCM (Fuzzy c-means) clustering algorithm and RBF (Radial basis function network) neural network model is proposed. This method is used to analyze the thermal error of the spindle reference point of the tauren EDM (Electro-discharge machining) machine tool. The thermal imager is used to obtain the temperature field distribution of the machine tool while the machine tool simulates actual operating conditions. Based on this, the arrangement of temperature measurement points is determined, and the temperature data of the corresponding measurement points are got by temperature sensors. In actual engineering, too many temperature measurement points can cause problems such as too high cost, too much wiring. And normal processing can be affected. In order to establish that the thermal error prediction model of the machine tool spindle reference point can meet the actual engineering needs, the adaptive FCM clustering algorithm is used to optimize the temperature measurement points. While collecting the temperatures of the optimized temperature measurement points, the displacement sensors are used to detect the thermal deformation data in X, Y, Z directions of the spindle reference position. Based on the test data, the RBF neural network thermal errors prediction model of the machine tool spindle reference point is established. Then, the test results are used to verify the accuracy of the thermal errors analysis model. The research method in this paper provides a system solution for thermal error analysis of the tauren EDM machine tool. And this builds a foundation for real-time compensation of the machine tool’s thermal errors.

Design of Vehicle Stability Controller Based on Fuzzy Radial Basis Neural Network Sliding Mode Theory with Sideslip Angle Estimation

This study is targeted at the key state parameters of vehicle stability controllers, the controlled vehicle model, and the nonlinearity and uncertainty of external disturbance. An adaptive double-layer unscented Kalman filter (ADUKF) is used to compute the sideslip angle, and a vehicle stability control algorithm adaptive fuzzy radial basis function neural network sliding mode control (AFRBF-SMC) is proposed. Since the sideslip angle cannot be directly determined, a 7-degrees-of-freedom (DOF) nonlinear vehicle dynamic model is established and combined with ADUKF to estimate the sideslip angle. After that, a vehicle stability sliding mode controller is designed and used to trace the ideal values of the vehicle stability parameters. To handle the severe system vibration due to the large robustness coefficient in the sliding mode controller, we use a fuzzy radial basis function neural network (FRBFNN) algorithm to approximate the uncertain disturbance of the system. Then the adaptive rate of the system is solved using the Lyapunov algorithm, and the systemic stability and convergence of this algorithm are validated. Finally, the controlling algorithm is verified through joint simulation on MATLAB/Simulink-Carsim. ADUKF can estimate the sideslip angle with high precision. The AFRBF-SMC vehicle stability controller performs well with high precision and low vibration and can ensure the driving stability of vehicles.

Self-organized hybrid fuzzy neural networks driven with the aid of probability-based node selection and enhanced input strategy

Polynomial neural network (PNN) is a flexible self-organizing network model, and there have been lots of improvements to it. However, various and improved models of PNN do not properly balance their performance and efficiency. In this study, we present novel self-organized hybrid fuzzy neural networks (SHFNN). The purpose of this study is to develop a new design methodology of constructing a hybrid fuzzy model through enhanced input strategy (EIS) and probability-based node selection (PNS) to reinforce the performance of the model without sacrificing efficiency. l2-norm regularization (l2) is utilized to mitigate overfitting as well as enhance generalization capability. The key points of SHFNN can be summarized as follows. First, a hybrid network structure is constructed by combining fuzzy radial basis function neural networks (FRBFNN) and PNN. Second, we propose a probability-based node selection strategy for node (neuron) selection. Third, an enhanced input strategy (EIS) is proposed to design the enhanced input set by merging the general candidate input set (GCIS) and the original candidate input set (OCIS). The regularization coefficient estimation method based on l2 is used in conjunction with EIS and PNS to improve the performance and enhance the stability of the model. The effectiveness of SHFNN is validated using synthetic and machine learning dataset. The experimental results show that the prediction accuracy of the proposed model is improved by 4–22% when compared with some methods reported in the literature. Statistical analysis is also considered to demonstrate the superiority of the proposed model.

Optimasi Fuzzy Artificial Neural Network dengan Algoritma Genetika untuk Prediksi Harga Crude Palm Oil

Crude Palm Oil (CPO) is one of Indonesia's best export commodities. CPO production competition causes price fluctuations so that it can trigger losses. The solution that can be taken to avoid losses is to predict the price of CPO. Time series data in the previous months, starting from January 2009 until January 2020, are used as a reference to predict the next CPO price. In this research, CPO price prediction is carried out with a combination of artificial intelligence concepts, namely Radial Basis Function Neural Network (RBFNN), and fuzzy logic. The combination of these methods, namely Fuzzy Radial Basis Function Neural Network (FRBFNN), is then optimized using genetic algorithms. The prediction results show that the error based on the MAPE value for FRBFNN prediction on training data is 11.7% and the MAPE value for testing data is 9.4%. In the FRBFNN prediction that was optimized using a genetic algorithm, the MAPE value was 10.2% for training data and 8.3% for testing data.

Short-Term Road Speed Forecasting Based on Hybrid RBF Neural Network With the Aid of Fuzzy System-Based Techniques in Urban Traffic Flow

With the rapid economic development, urban areas are seeing more and more vehicles, leading to frequent urban traffic congestion. To solve this problem, the forecasting of traffic parameters is essential, in which, road operating speed (hereinafter referred to as “road speed”) is a key parameter for forecasting road congestion. This paper proposes a hybrid radial basis function (RBF) neural network algorithm for forecasting road speed. First, it proposes a fuzzy RBF neural network structure by combining the fuzzy logic system with the RBF neural network. Then, it incorporates factors such as weather, holidays and road grades into the input layer. Considering the uncertainty and sensitivity of the initial centre of the traditional membership function layer, it uses fuzzy C-means clustering to determine the centre and other parameters of the membership function layer. Then using the gradient descent method, it trains the weights between the fuzzy inference layer and the output layer. Finally, this paper trains the proposed hybrid RBF neural network with the traffic road network data and weather data of a city, and uses the trained hybrid neural network to predict the road speed and the congestion status. The prediction results show that, compared with simplex prediction methods, such as BP neural network, time series method, and RBF neural network, the hybrid RBF neural network has a higher forecasting accuracy, with the mean absolute percentage error (MAPE) being reduced to 6.4%. Experimental results verify the accurate forecasting, enhanced learning feature and mapping capability of this method in short-term road speed forecasting, indicating that it can provide reliable predicted values to help solve urban congestion problems.

Fuzzy Neural Network Q-Learning Method for Model Disturbance Change: A Deployable Antenna Panel Application

This paper addresses the disturbance change control problem with an active deformation adjustment mechanism on a 5-meter deployable antenna panel. A fuzzy neural network Q-learning control (FNNQL) strategy is proposed in this paper for the disturbance change to improve the accuracy of the antenna panel. In the proposed method, the error of the model disturbance is reduced by introducing the fuzzy radial basis function (RBF) neural network into Q-learning, and the parameters of the fuzzy RBF neural network were optimized and adjusted by a Q-learning method. This allows the FNNQL controller to have a strong adaptability to deal with the disturbance change. Finally, the proposed method has been adopted in the middle plate of a 5-meter deployable antenna panel, and it was found that the method could successfully adapt the model disturbance change in the antenna panel. Results of the simulation also show that the whole control system meets the required accuracy requirements.

Egg freshness recognition based on a fuzzy radial-basis-function neural network technology

There are often mobile, unconscientious vendors in China who sell stale or even rotten eggs in produce markets, earning money by dishonest means. To prevent the entry of substandard eggs into the market that endanger the health of consumers, we designed an egg freshness recognition system based on a fuzzy radial-basis-function (RBF) neural network. This system acquires the color characteristic parameters red (R), green (G), and blue (B) of the transmitted light of eggs through a computer vision device. The system converts the RGB values into HIS (hue, intensity, and saturation) values, uses the egg transmitted light color characteristic parameter HIS as an input value and employs the Huff value coding as an output. A test sample was used to verify the identification system. Our experimental results show that when using a fuzzy RBF neural network and simple RBF neural network algorithm, the average recognition accuracy of the system is 96.35% and 92.72%, respectively, both of which are higher than the average recognition accuracy of 88.14% when using the back propagation neural network algorithm. The feasibility and superiority of the identification system proposed in this paper were verified; therefore, this system may serve as a reference for future research on egg freshness recognition.

Design of fuzzy radial basis function neural network classifier based on information data preprocessing for recycling black plastic wastes: comparative studies of ATR FT-IR and Raman spectroscopy

As large amounts of plastics are widely used in diverse areas of industry, the amount of plastic waste, including black plastics, continues to increase. In this situation, the necessity of useful recycling having limited resources gradually increases. The design of plastic classification systems for plastics recycling becomes more important to effectively address recycling activities. Until now, conventional sorting systems based on the near infrared ray technology have been used to classify plastic wastes. However, the classification of black plastic waste still remains a challenge because such materials do not reflect sufficient signals due to the absorption of laser light coming from the NIR spectrometer. In order to solve such problems, this research is focused on an efficient way to identify black plastics. Attenuated Total Reflectance (ATR) Fourier Transform Infrared Radiation (FT-IR) and a Raman spectrometer are used to carry out qualitative and quantitative analysis for the effective as well as efficient classification of black plastic wastes. In this study, to effectively classify the black plastic waste, data processing and Fuzzy Transform (F-Transform) as well as PCA-based Fuzzy Radial Basis Function Neural Networks (FRBFNNs) classifier is proposed. Input variables extracted on a basis of chemical characteristic peaks as well as interval range positioned near the chemical characteristic peaks were exploited as a way to improve the classification performance of the FRBFNN classifier. In order to evaluate the performance of the classifier, a suite of techniques including F-Transform-based as well as Principal Component Analysis (PCA)-based FRBFNNs classifier designed with the aid of Particle Swam Optimization are developed to analyze and classify black plastics.

Design of face recognition system based on fuzzy transform and radial basis function neural networks

In this study, a face recognition method based on fuzzy transform and radial basis function neural networks is proposed. In order to reduce the dimensionality and extract the important features of face images, fuzzy transform with fuzzy partition techniques is used. Fuzzy radial basis function neural networks (FRBFNNs) are used as a classifier to identify face images into several categories. Radial basis functions are defined by fuzzy C-means clustering method which can analyze the distribution of data points over the input spaces. In order to validate the proposed face recognition system, experimental comparative studies are conducted on the benchmark face datasets such as YALE, ORL, and ABERDEEN databases. A comparative analysis demonstrates that the proposed face recognition system is superior to the conventional face recognition techniques.

Multi-modality image fusion based on enhanced fuzzy radial basis function neural networks

In clinical applications, single modality images do not provide sufficient diagnostic information. Therefore, it is necessary to combine the advantages or complementarities of different modalities of images. Recently, neural network technique was applied to medical image fusion by many researchers, but there are still many deficiencies. In this study, we propose a novel fusion method to combine multi-modality medical images based on the enhanced fuzzy radial basis function neural network (Fuzzy-RBFNN), which includes five layers: input, fuzzy partition, front combination, inference, and output. Moreover, we propose a hybrid of the gravitational search algorithm (GSA) and error back propagation algorithm (EBPA) to train the network to update the parameters of the network. Two different patterns of images are used as inputs of the neural network, and the output is the fused image. A comparison with the conventional fusion methods and another neural network method through subjective observation and objective evaluation indexes reveals that the proposed method effectively synthesized the information of input images and achieved better results. Meanwhile, we also trained the network by using the EBPA and GSA, individually. The results reveal that the EBPGSA not only outperformed both EBPA and GSA, but also trained the neural network more accurately by analyzing the same evaluation indexes.

FPGA Based ASFSRBN Controller for Low Offset Integrator for Long Pulse Operation in Aditya Tokamak

Tokamaks are the most proficient devices for acquiring nuclear fusion energy from high-temperature, ionized gas termed as Plasma. Mostly, tokamaks are utilized the toroidal magnetic plasma component and poloidal magnetic plasma component to retain and automatic control. Similar characteristics of two operational amplifiers should be needed in the integration system to calculate the drift value. Inductive magnetic sensors are routinely utilized on existing Tokamaks, are well comprehended and they oblige signal integration to focus the strength of the magnetic field. However, the signal integration failed to solve the problem of deciding the stability of the controlled systems. The Sample and Hold Amplifier (SHA) is an essential part of the sampling (Analog Devices, 2009) to digital convert (ADC). The proposed method is used for eliminating the offset drift of the Integrator for long pulse operations. Adaptive Self Organizing Fuzzy Radial Basis-Function Neural Network Controller (ASFRBNC) and DAC amplifiers are able to adjust the signal for utilization of the PCS and DAS dynamic ranges. The loop processing has done with MATLAB Simulink process. This tokamak has 100s pulse lengths, oblige integrators with better attributes in drift and noise with the long pulse lengths. The proposed system was implemented in Field Programmable Gate Array (FPGA) chip with the use of Verilog Hardware Description-Language (Verilog HDL). The experimental results shown that the proposed method obtained 745 Slices, 61 Flip-Flops (FF), 46 I/O buffers (IOBs), 1221 4bit Look up Tables (LUT) and synchronized with the maximum 50.36MHz clock frequency. The obtained hardware results have been matched with the MATLAB simulation results.

Modeling Fuzzy RBF Neural Network to Predict of Mechanical Properties of Welding Joints Based on Fuzzy C-means Cluster

For high nonlinear, complex interaction of many factors in welding process, it is difficult to predict the mechanical properties of welded joints. At the same time to overcome the lack of common back propagation(BP) neural network. A fuzzy radial basis function(RBF) neural network model is built to predict mechanical properties of welded joints based on Fuzzy C-means cluster and Pseudo-inverse method. Take the TC4 titanium alloy Tungsten inert gas(TIG) arc welding process parameters including:Welding current, welding speed, argon gas flux for the input parameters, take the mechanical properties of welded joints including: Tensile strength, bend strength, extensibility, weld hardness and heat affected zone hardness for the output parameters. The 27 sets experiment data are used to train this model, other 9 sets are used to simulate this model. Simulation results show that the model's structural is stability, training speed is fast, adaptability is strong, robustness is good, prediction accuracy is high, it can be used to predict the mechanical properties of welded joints. Through mathematical analysis, it can express the rule between welding process parameters and mechanical properties of joints use functional from, also can be used to optimize the welding parameters, provide the basis to adjust and control the quality of welded joints.

Face Recognition Based on Improved Fuzzy RBF Neural Network for Smar t Device

Face recognition is a science of automatically identifying individuals based their unique facial features. In order to avoid overfitting and reduce the computational reduce the computational burden, a new face recognition algorithm using PCA-fisher linear discriminant (PCA-FLD) and fuzzy radial basis function neural network (RBFNN) is proposed in this paper. First, face features are extracted by the principal component analysis (PCA) method. Then, the extracted features are further processed by the Fisher's linear discriminant technique to acquire lower-dimensional discriminant patterns, the processed features will be considered as the input of the fuzzy RBFNN. As a widely applied algorithm in fuzzy RBF neural network, BP learning algorithm has the low rate of convergence, therefore, an improved learning algorithm based on Levenberg-Marquart (L-M) for fuzzy RBF neural network is introduced in this paper, which combined the Gradient Descent algorithm with the Gauss-Newton algorithm. Experimental results on the ORL face database demonstrate that the proposed algorithm has satisfactory performance and high recognition rate.

Instability identification on large scale underground mined-out area in the metal mine based on the improved FRBFNN

To identify the instability on large scale underground mined-out area in the metal mine effectively, the parameters of radial basis function were determined through clustering method and the improved fuzzy radial basis function neural network (FRBFNN) model of instability identification model about large scale underground mined-out area in the metal mine was built. The improved FRBFNN model was trained and tested. The results show that the improved FRBFNN model has high training accuracy and generalization ability. Parameters such as pillar area ratio, filling level and the value of rock quality designation have strong influence on instability of large scale underground mined-out area. Correctness of analysis about the improved FRBFNN model was proved by the practical application results about instability discrimination of surrounding rock in large-scale underground mined-out area of a metal mine in south China.

Fuzzy Radial Basis Function Neural Networks with information granulation and its parallel genetic optimization

Fuzzy modeling of complex systems is a challenging task, which involves important problems of dimensionality reduction and calls for various ways of improving the accuracy of modeling. The IG-FRBFNN, a hybrid architecture of the IG-FIS (Fuzzy Inference System) and FRBFNN (Fuzzy Radial Basis Function Neural Networks), is proposed to address these problems. The paper is concerned with the analysis and design of IG-FRBFNNs and their optimization by means of the Hierarchical Fair Competition-based Parallel Genetic Algorithm (HFC-PGA). In the proposed network, the membership functions of the premise part of the fuzzy rules of the IG-based FRBFNN model directly rely on the computation of the relevant distance between data points and the use of four types of polynomials such as constant, linear, quadratic and modified quadratic are considered for the consequent part of fuzzy rules. Moreover, the weighted Least Square (WLS) learning is exploited to estimate the coefficients of the polynomial forming the conclusion part of the rules. Since the performance of the IG-RBFNN model is affected by some key design parameters, such as a specific subset of input variables, the fuzzification coefficient of the FCM, the number of rules, and the order of polynomial of the consequent part of fuzzy rules, it becomes beneficial to carry out both structural as well as parametric optimization of the network. In this study, the HFC-PGA is used as a comprehensive optimization vehicle. The performance of the proposed model is illustrated by means of several representative numerical examples.

Millimeter wave image restoration based on fuzzy radial basis function neural networks and sparse representation

As to the problems that Millimeter Wave(MMW) image is contaminated by much unknown noise and has lower resolution,and considering the non-linear filter property of Fuzzy Radial Basis Function Neural Network(F-RBFNN) and the self-adaptive denoising property of Sparse Representation(SR) based on K-Singular Value Decomposition(K-SVD),a MMW restoration method was proposed by combining F-RBFNN and sparse representation.In F-RBFNN,the knowledge expression of fuzzy logic and the reasoning ability were combined with the RBFNN's capabilities of fast learning and generalization.In order to realize the non-linear filtering to the MMW image,F-RBFNN's structure and parameters were adjusted according to the real problem.Furthermore,utilizing the advantages of sparse representation method,which the sparse representation behaves the visual characteristic and can denoise effectively when maintaining features of the object,the training results of F-RBFNN were locally denoised once again,and the MMW image with high resolution was obtained.Using the Relative Single Noise Ratio(RSNR) criterion to measure the quality of denoised images,the simulation results show that,compared with other denoising methods such as F-RBFNN,K-SVD denoising,and wavelet denoising,the proposed method combining F-RBFNN and SR can better restore the quality of MMW image.

Application of Immune Genetic Algorithm Based Fuzzy RBF Neural Network in High-speed Motorized Spindles

To satisfy the control requirements of fast, stable, accurate in the high speed and high performance motorized spindle system, a global optimization control strategy of high-speed motorized spindle which is the organic combination of fuzzy logic control, radial basis function(RBF) neural network and immune genetic algorithm is proposed. The control strategy combines the advantages of fast searching optimization of immune genetic algorithm and independence on spindle system model of fuzzy neural network, and this intelligent control strategy is successfully applied to the speed controller of double closed-loop vector control system for high-speed motorized spindle. The simultaneous optimization of the three types parameters of the intelligent controller can achieve optimal control effect through using IGA, and successfully implement the accurate speed control process. This strategy can accurately control spindle speed and perform quite good anti-interference ability and strong robustness when spindle bears instant impact load, which is verified through experimental and simulation results, and both dynamic and steady performance are improved evidently. The spindle system can achieve high-quality drive finally.

Multiobjective Space Search Optimization and Information Granulation in the Design of Fuzzy Radial Basis Function Neural Networks

This study introduces an information granular-based fuzzy radial basis function neural networks (FRBFNN) based on multiobjective optimization and weighted least square (WLS). An improved multiobjective space search algorithm (IMSSA) is proposed to optimize the FRBFNN. In the design of FRBFNN, the premise part of the rules is constructed with the aid of Fuzzy C-Means (FCM) clustering while the consequent part of the fuzzy rules is developed by using four types of polynomials, namely constant, linear, quadratic, and modified quadratic. Information granulation realized with C-Means clustering helps determine the initial values of the apex parameters of the membership function of the fuzzy neural network. To enhance the flexibility of neural network, we use the WLS learning to estimate the coefficients of the polynomials. In comparison with ordinary least square commonly used in the design of fuzzy radial basis function neural networks, WLS could come with a different type of the local model in each rule when dealing with the FRBFNN. Since the performance of the FRBFNN model is directly affected by some parameters such as e.g., the fuzzification coefficient used in the FCM, the number of rules and the orders of the polynomials present in the consequent parts of the rules, we carry out both structural as well as parametric optimization of the network. The proposed IMSSA that aims at the simultaneous minimization of complexity and the maximization of accuracy is exploited here to optimize the parameters of the model. Experimental results illustrate that the proposed neural network leads to better performance in comparison with some existing neurofuzzy models encountered in the literature.

Research for RBF Neural Networks Modeling Accuracy of Determining the Basis Function Center Based on Clustering Methods

The radial basis function (RBF) neural network is superior to other neural network on the aspects of approximation ability, classification ability, learning speed and global optimization etc., it has been widely applied as feedforward networks, its performance critically rely on the choice of RBF centers of network hidden layer node. K-means clustering, as a commonly method used on determining RBF center, has low neural network generalization ability, due to its clustering results are not sensitive to initial conditions and ignoring the influence of dependent variable. In view of this problem, fuzzy clustering and grey relational clustering methods are proposed to substitute K-means clustering, RBF center is determined by the results of fuzzy clustering or grey relational clustering, and some researches of RBF neural networks modeling accuracy are done. Practical modeling cases demonstrate that the modeling accuracy of fuzzy clustering RBF neural networks and grey relational clustering RBF neural networks are significantly better than K-means clustering RBF neural networks, applying of fuzzy clustering or grey relational clustering to determine the basis function center of RBF neural networks hidden layer node is feasible and effective.