Compensatory Fuzzy Neural Network Research Articles

Remote sensing image information extraction based on Compensated Fuzzy Neural Network and big data analytics

Medical imaging AI systems and big data analytics have attracted much attention from researchers of industry and academia. The application of medical imaging AI systems and big data analytics play an important role in the technology of content based remote sensing (CBRS) development. Environmental data, information, and analysis have been produced promptly using remote sensing (RS). The method for creating a useful digital map from an image data set is called image information extraction. Image information extraction depends on target recognition (shape and color). For low-level image attributes like texture, Classifier-based Retrieval(CR) techniques are ineffective since they categorize the input images and only return images from the determined classes of RS. The issues mentioned earlier cannot be handled by the existing expertise based on a keyword/metadata remote sensing data service model. To get over these restrictions, Fuzzy Class Membership-based Image Extraction (FCMIE), a technology developed for Content-Based Remote Sensing (CBRS), is suggested. The compensation fuzzy neural network (CFNN) is used to calculate the category label and fuzzy category membership of the query image. Use a basic and balanced weighted distance metric. Feature information extraction (FIE) enhances remote sensing image processing and autonomous information retrieval of visual content based on time-frequency meaning, such as color, texture and shape attributes of images. Hierarchical nested structure and cyclic similarity measure produce faster queries when searching. The experiment’s findings indicate that applying the proposed model can have favorable outcomes for assessment measures, including Ratio of Coverage, average means precision, recall, and efficiency retrieval that are attained more effectively than the existing CR model. In the areas of feature tracking, climate forecasting, background noise reduction, and simulating nonlinear functional behaviors, CFNN has a wide range of RS applications. The proposed method CFNN-FCMIE achieves a minimum range of 4–5% for all three feature vectors, sample mean and comparison precision-recall ratio, which gives better results than the existing classifier-based retrieval model. This work provides an important reference for medical imaging artificial intelligence system and big data analysis.

Research on the Application of Gray Prediction and BP Neural Network in English-Chinese Translation Quality Assessment

Abstract Gray prediction model and BP neural network are increasingly used in many fields as important prediction and analysis tools. The article first elaborates on the related algorithm of the gray prediction method. It constructs the gray prediction model, then constructs the BP neural network, and builds the gray BP neural network translation quality assessment model through the modeling of the simulation of the artificial neural network, and finally takes eight postgraduate students of translation majors of Beijing Foreign Studies University as the subjects, and the eight students translate two articles with similar word counts and difficulties, and score the translation quality of the subjects, and reasonably evaluate and scientifically predict these translations in order to validate the grey translation quality of subjects. The quality of translation is scored, and these translations are reasonably evaluated and scientifically predicted to verify the role and effect of the gray BP neural network in the quality of English-Chinese translation. The study concludes that the results based on the gray prediction model are that the prediction results are in the error range of 0%–7% compared with the original data, and the overall error is small. The prediction results of the compensatory fuzzy neural network prediction model for all 8 subjects before and after translation were more accurate than those predicted by the traditional fuzzy neural network method, with a good fit to the actual monitoring data and a small relative error. Only one subject out of the 8 spent more time on Chapter B than on Chapter A. Chapter B was more time-consuming than Chapter A. The time spent on Chapter A was more than the time spent on Chapter A. Chapter B was more time-consuming than Chapter A. Chapter B was more time-consuming than Chapter A. One subject spent equal time on both articles. The remaining 6 subjects did not spend as much time on chapter B as they did on chapter A.

Automatic Sleep Stage Classification Using a Taguchi-Based Multiscale Convolutional Compensatory Fuzzy Neural Network

Current methods for sleep stage detection rely on sensors to collect physiological data. These methods are inaccurate and take up considerable medical resources. Thus, in this study, we propose a Taguchi-based multiscale convolutional compensatory fuzzy neural network (T-MCCFNN) model to automatically detect and classify sleep stages. In the proposed T-MCCFNN model, multiscale convolution kernels extract features of the input electroencephalogram signal and a compensatory fuzzy neural network is used in place of a traditional fully connected network as a classifier to improve the convergence rate during learning and to reduce the number of model parameters required. Due to the complexity of general deep learning networks, trial and error methods are often used to determine their parameters. However, this method is very time-consuming. Therefore, this study uses the Taguchi method instead, where the optimal parameter combination is identified over a minimal number of experiments. We use the Sleep-EDF database to evaluate the proposed model. The results indicate that the proposed T-MCCFNN sleep stage classification accuracy is 85.3%, which is superior to methods proposed by other scholars.

Retracted: Automatic Detection Method of Technical and Tactical Indicators for Table Tennis Based on Trajectory Prediction Using Compensation Fuzzy Neural Network.

[This retracts the article DOI: 10.1155/2021/3155357.].

Neural-Observer-Based Terminal Sliding Mode Control: Design and Application

In view of the fact that most devices are nonlinear systems, this article proposes a recurrent probabilistic compensation fuzzy neural network (RPCFNN) control scheme based on global fast terminal sliding mode control (GFTSMC) for a class of nonlinear systems with uncertainties. First, GFTSMC is developed to impose a finite-time convergence feature for the considered systems. Second, a novel RPCFNN framework is designed to further enhance the ability to deal with uncertainty. Due to the added probabilistic estimation and dynamic fuzzy operator, the developed RPCFNN controller possesses superior nonlinearity handling capability. Meanwhile, the stability of RPCFNN is proved by the Lyapunov theory. Finally, active power filter is taken as the representative of nonlinear systems to verify the effectiveness and feasibility of the proposed method. Simulation and experimental results show that the novel RPCFNN controller has good steady-state and dynamic performance and has excellent robustness to deal with uncertain disturbances.

A Mobile Intelligent Mine Platform with a Hybrid Fuzzy NN and ATT-CNN Prewarning Model

Given the existence of coal production risk, effective prewarning is important to the reliability and safety of coal mine. So, the development of a risk prewarning system has become an important safety management tool. To improve the prediction ability and the supervision level of safety production, and handle different multidimensional (temporal and spatial) information for risk prewarning, we built a new platform based on the Internet, cloud platform, mobile communication, GIS, and artificial intelligence technology, i.e., a mobile intelligent mine platform. The terminal of the platform provides real-time queries and procedures of coal mine production and risk prewarning and provides data support and technical means for daily supervision, remote networking analysis, law enforcement inspection, and emergency rescue. The prewarning model of safety risk is an essential means to realize prewarning. The complexity of production of coal mine leads to the dynamic characteristics, fuzziness, and randomness of coal mine accidents. The complex nonlinear relationship between index and risk level leads to low accuracy of the traditional back propagation (BP) neural network prewarning method. A novel model based on a compensation fuzzy neural network (NN) and an attention mechanism-convolutional neural network (ATT-CNN) are a critical part of the new design. First, to full use of the convolutional network to get a larger receptive field, one-dimensional time series is transformed into two-dimensional matrix as the input of the CNN network by mapping. The neural network is utilized to extract the advanced features of the input signal. The results are finally output through a fully connected classifier. The model fuses multisource data at the feature level, employs the temporal and spatial relationships of monitoring data, and dynamically evaluates the risk. The experiment shows that the proposed model achieves impressive performance in both quantitative and qualitative evaluations and has improved the model generalization ability. The combination of data integration, remote examinations, and approval from existing information systems enables this platform to provide dynamic reminders of approval information, various risk prewarning, and management process automation through a mobile network.

A Novel Data-Driven Estimation Method for State-of-Charge Estimation of Li-Ion Batteries

With the increasing proportion of Li-ion batteries in energy structures, studies on the estimation of the state of charge (SOC) of Li-ion batteries, which can effectively ensure the safety and stability of Li-ion batteries, have gained much attention. In this paper, a new data-driven method named the probabilistic threshold compensation fuzzy neural network (PTCFNN) is proposed to estimate the SOC of Li-ion batteries. Compared with other traditional methods that need to build complex battery models, the PTCFNN only needs data learning to obtain nonlinear mapping relationships inside Li-ion batteries. In order to avoid the local optimal value problem of traditional BP neural networks and the fixed reasoning mechanism of traditional fuzzy neural networks, the PTCFNN combines the advantages of a probabilistic fuzzy neural network and a compensation fuzzy neural network so as to improve the learning convergence speed and optimize the fuzzy reasoning mechanism. Finally, in order to verify the estimation performance of the PTCFNN, a 18650-20R Li-ion battery was used to carry out the estimation test. The results show that the mean absolute error and mean square error are very small under the conditions of a low-current test and dynamic-current test, and the overall estimation error is less than 1%, which further indicates that this method has good estimation ability.

Model Reference Adaptive Control and Fuzzy Neural Network Synchronous Motion Compensator for Gantry Robots

A model reference adaptive control and fuzzy neural network (FNN) synchronous motion compensator for a gantry robot is presented in this paper. This paper proposes the development and application of gantry robots with MRAC and FNN online compensators. First, we propose a model reference adaptive controller (MRAC) under the cascade control method to make the reference model close to the real model and reduce tracking errors for the single axis. Then, a fuzzy neural network compensator for the gantry robot is proposed to compensate for the synchronous errors between the dual servo motors to improve precise movement. In addition, an online parameter training method is proposed to adjust the parameters of the FNN. Finally, the experimental results show that the proposed method improves the synchronous errors of the gantry robot and demonstrates the methodology in this paper. This study also successfully integrates the hardware and successfully verifies the proposed methods.

Automatic Detection Method of Technical and Tactical Indicators for Table Tennis Based on Trajectory Prediction Using Compensation Fuzzy Neural Network.

In the system design of table tennis robot, the important influencing factors of automatic detection of technical and tactical indicators for table tennis are table tennis rotation state, trajectory, and rebound force. But the general prediction algorithm cannot process the time series data and give the corresponding rotation state. Therefore, this paper studies the automatic detection method of technical and tactical indicators for table tennis based on the trajectory prediction using the compensation fuzzy neural network. In this paper, the compensation fuzzy neural network algorithm which combines the compensation fuzzy algorithm and recurrent neural network is selected to construct the automatic detection of technical and tactical indicators for table tennis. The experimental results show that the convergence time of the compensation fuzzy neural network is shorter, the training time is shortened, and the prediction accuracy is improved. At the same time, in terms of performance testing, the model can accurately distinguish the influence of table tennis rotation state and rebound on table tennis motion estimation, so as to improve the accuracy of motion trajectory prediction. In addition, the accuracy of trajectory prediction will be improved with the increase of input data. When the number of data reaches 30, the trajectory prediction error is within the actual acceptable error range.

Control Strategy of Braking Energy Recovery for Range‐Extended Electric Commercial Vehicles by Considering Braking Intention Recognition and Electropneumatic Braking Compensation

Braking energy recovery is a key technology for improving energy efficiency and extending the driving range of electric vehicles. However, there are challenges for maximizing energy recovery and improving braking sense. To solve these problems, a hierarchical control strategy of braking energy recovery that considers braking intention recognition and electropneumatic braking compensation is proposed herein. For the upper controller, Layer Hidden Markov Model‐Dynamic Compensatory Fuzzy Neural Network is applied to recognize the driver's braking intention. For the medium controller, a braking force predistribution strategy based on braking intention recognition results is designed. For the lower controller, an electropneumatic braking compensation method is used to reduce the adverse effects due to the composite braking system. Subsequently, vehicle tests in single‐braking conditions and hardware in loop (HIL) simulation in the World Transient Vehicle Cycle (C‐WTVC) are conducted to verify the performance of the control strategy. The results indicate that the proposed control strategy optimizes the braking force in all the tests and simulations, and the energy recovery rate reaches 12.75% in C‐WTVC, thereby surpassing that of the other three control strategies. Therefore, the results of the study indicate an improvement in the economic performance and ride comfort of electric vehicles.

Intelligent PV Power System With Unbalanced Current Compensation Using CFNN-AMF

A novel method is proposed to compensate the three-phase unbalanced currents of power grid under three-phase unbalanced load for a two-stage photovoltaic (PV) power system without the augmentation of active power filter. The PV power system is composed of an interleaved dc/dc converter and a three-level neutral-point clamped inverter. Moreover, the PV power system possesses the smart inverter function, in which the output active and reactive powers of the PV inverter are predetermined by a power factor according to grid codes of the utilities. In the proposed method, $dq0$ -axis compensation currents are obtained through low-pass filters to compensate the three-phase unbalanced currents of power grid. Furthermore, in order to improve the control performance of the dc bus voltage of the PV power system under unbalanced load variation condition, an online-trained compensatory neural fuzzy network with an asymmetric membership function (CFNN-AMF) is proposed to replace the traditional proportional-integral controller for the dc bus voltage control. In the proposed CFNN-AMF, the compensatory parameter to integrate pessimistic and optimistic operations of fuzzy systems is embedded in the CFNN. In addition, the dimensions of the Gaussian membership functions are directly extended to AMFs. Additionally, the proposed controllers of the PV power system are implemented by two control platforms using floating-point digital signal processor. Finally, excellent compensation performance for the three-phase currents of power grid under three-phase unbalanced load can be achieved from the experimental results.

Adaptive Backstepping Fuzzy Neural Network Fractional-Order Control of Microgyroscope Using a Nonsingular Terminal Sliding Mode Controller

An adaptive fractional-order nonsingular terminal sliding mode controller for a microgyroscope is presented with uncertainties and external disturbances using a fuzzy neural network compensator based on a backstepping technique. First, the dynamic of the microgyroscope is transformed into an analogical cascade system to guarantee the application of a backstepping design. Then, a fractional-order nonsingular terminal sliding mode surface is designed which provides an additional degree of freedom, higher precision, and finite convergence without a singularity problem. The proposed control scheme requires no prior knowledge of the unknown dynamics of the microgyroscope system since the fuzzy neural network is utilized to approximate the upper bound of the lumped uncertainties and adaptive algorithms are derived to allow online adjustment of the unknown system parameters. The chattering phenomenon can be reduced simultaneously by the fuzzy neural network compensator. The stability and finite time convergence of the system can be established by the Lyapunov stability theorem. Finally, simulation results verify the effectiveness of the proposed controller and the comparison of root mean square error between different fractional orders and integer order is given to signify the high precision tracking performance of the proposed control scheme.

A Distribution Static Compensator Using a CFNN-AMF Controller for Power Quality Improvement and DC-Link Voltage Regulation

A distribution static compensator (DSTATCOM) is proposed in this study to improve the power quality, which includes the total harmonic distortion (THD) of the grid current and power factor (PF), of a mini grid with nonlinear and linear inductive loads. Moreover, the DC-link voltage regulation control of the DSTATCOM is essential especially under load variation conditions. Therefore, to improve the power quality and keep the DC-link voltage of the DSTATCOM constant under the variation of nonlinear and linear loads effectively, the traditional proportional-integral (PI) controller is substituted with a new online trained compensatory fuzzy neural network with an asymmetric membership function (CFNN-AMF) controller. In the proposed CFNN-AMF, the compensatory parameter to integrate pessimistic and optimistic operations of fuzzy systems is embedded in the CFNN. Furthermore, the dimensions of the Gaussian membership functions are directly extended to AMFs for the optimization of the fuzzy rules and the upgrade of learning ability of the networks. In addition, the network structure and online learning algorithm of the proposed CFNN-AMF are introduced in detail. Finally, the effectiveness and feasibility of the DSTATCOM using the proposed CFNN-AMF controller to improve the power quality and maintain the constant DC-link voltage under the change of nonlinear and linear inductive loads have been demonstrated by some experimental results.

Digital signal processor based intelligent fractional‐order sliding‐mode control for a linear voice coil actuator

In this study, a digital signal processor (DSP) based intelligent fractional-order sliding-mode control (IFOSMC) is proposed to control a linear voice coil actuator (VCA) for the tracking of reference trajectory. First, a dynamic model of the VCA is analysed considering the system uncertainties. Subsequently, a sliding-mode control (SMC) and a fractional-order SMC (FOSMC) are developed for the VCA control system. With increased degrees of freedom for the control parameters, the FOSMC can improve the control performance of the SMC. However, because the uncertainties of the VCA are unclear, designing hitting control laws for the SMC and FOSMC is difficult. Thus, the IFOSMC is proposed for improving the adaptability and robustness of the control system. For the IFOSMC, a compensatory fuzzy neural network observer is designed to replace the hitting control directly while a switching compensator is developed to compensate for the observation error smoothly. A Lyapunov method is used to derive the adaptive laws for tuning the control parameters of the IFOSMC online. Experimentations regarding nominal and parameter variation cases were undertaken through the DSP. Experimental results show that the proposed IFOSMC significantly improves the control performances of the SMC and FOSMC with regard to the VCA control system.

Compensatory fuzzy neural network control with dynamic parameters estimation for linear voice coil actuator

The object of this study is to develop an intelligent control strategy, which comprises a compensatory fuzzy neural network (CFNN) controller with a dynamic particle swarm optimization (DPSO) based estimator, for on-line parameter estimation and control of a linear voice coil actuator (VCA). Because the plant Jacobian of the VCA is nonlinear and time-varying, it is difficult to derive the learning algorithm for the CFNN by using the conventional back-propagation (BP) method directly. Therefore, it is strongly desirable that an on-line manner can provide a reasonably good estimation of the plant Jacobian in the practical applications. In this study, the operating principle and dynamic analysis of the VCA are introduced first. Subsequently, the algorithms of the DPSO and CFNN are given where the DPSO and CFNN are utilized to obtain the control signal and estimate the plant Jacobian, respectively. Moreover, a convergence analyses is given to derive specific learning rates for ensuring the convergence of the control error. Finally, the proposed control strategy is implemented on a 32-bit floating-point digital signal processor (DSP) for experimental verification. Experimental results demonstrate the improved tracking performance and robustness of the proposed CFNN-DPSO controller with online Jacobian estimation compared with the conventional CFNN controller with constant one, for the VCA control system.

Civil Aviation Security Early Warning Mechanism Based on Compensation Fuzzy Neural Network

Civil Aviation Security Early Warning Mechanism Based on Compensation Fuzzy Neural Network

Research of Power Plant Boiler Control System Based on Compensatory Fuzzy Neural Network

Considering thermal power plant boiler’s nonlinear, large delay and time-varying, the paper proposes a compensatory fuzzy neural network control based on fuzzy control and neural network control. The compensatory fuzzy neural network is better than the PID controller and general fuzzy network controller in properties by using fuzzy inference and compensatory arithmetic. The paper makes a preliminary simulation using simulation tools of Matlab. And, the superiority of the compensatory fuzzy neural network control is proved by comparing the two kinds of simulation.

Compensatory neural fuzzy network with symbiotic particle swarm optimization for temperature control

This study proposes a symbiotic particle swarm optimization (SPSO) algorithm for compensatory neural fuzzy networks (CNFN). The CNFN model using compensatory fuzzy operators makes fuzzy logic systems more adaptive and effective. The proposed SPSO algorithm adopts a multiple swarm scheme that uses each particle to represent a single fuzzy rule and each particle in each swarm evolves separately to avoid falling into a locally optimal solution. Additionally, the SPSO embeds the symbiotic evolution scheme in a specific particle swarm optimization (PSO) to accelerate the search and increase global search capacity. Finally, the proposed CNFN with SPSO (CNFN-SPSO) method is applied to control a water bath temperature system. Results of this study demonstrate the effectiveness of the proposed CNFN-SPSO method.

The Study and Realization of Grading Modeling Risk Assessment for Buried Gas Pipeline

According to the risk assessment conducted on buried gas pipelines,it is found that the number of factors affecting pipeline failure is more than 130 and that it could not reach the requirement by using a model to calculate or evaluate.On the base of the traditional buried gas pipeline fault tree analysis, this paper puts forward a thought of the grading modeling risk assessment,adopting the compensation fuzzy neural network theory . The fault trees minimal cut sets grading modeling helps to establish the mathematical model of compensation fuzzy neural network risk assessment, deduce the model error transformation formula, revise the assessment errors ,and solve the problem of risk assessment of the large buried gas pipelineswhich needs to consider many assessment factors. The practical study demonstrates that the assessment results are objective and the assessment errors are small.

A hybrid method based on dynamic compensatory fuzzy neural network algorithm for face recognition

Human face recognition skills can make simultaneous use of a variety of information from the face, including information about the age, sex, race, identity, and even current mood of the person. In this paper, a hybrid method combined Eigenface-LDA with Dynamic Compensatory Fuzzy Neural Network (DCFNN) is proposed for face recognition. Eigenfaces-LDA algorithm is used for face image of dimensionality reduction and finding a best subspace for classification, the extracted feature will be considered as the input of DCFNN. An improved Dynamic Fuzzy Neural Network is proposed by combing Dynamic Fuzzy Neural Network and Compensatory Fuzzy Neural Network to solve the problem of feature classification. The proposed method has been tested on ORL and Yale face database; the experimental results show that our method can reduce the dimension of facial features well and recognize faces that under different illumination, pose and expression accurately.