Fuzzy Wavelet Neural Network Research Articles

Discrete-Time Event-Triggered Type-2 fuzzy wavelet neural network control for Multi-Motor servo system

This paper investigates a discrete-time event-triggered adaptive neural network control scheme for a multi-motor servo system (MMSS) to realize a desired position tracking performance. First, a discrete-time model of the MMSS, including a dead-zone and a nonlinear friction, is established based on the Euler’s discretization. Then, a discrete-time adaptive neural network controller is designed by integrating a type-2 fuzzy wavelet neural network (T2FWNN) and the backstepping technology. The neural network is not only used to estimate uncertain nonlinearities, but also can handle the non-causal problem caused by the conventional backstepping method. Meanwhile, a fixed threshold event-triggered mechanism along with the incorporation of a dead-zone operator is superimposed into the actual controller thus saving communicational resources. Besides, stability analysis proves that all the signals in the closed MMSS are bounded, and the position tracking error converges to a small neighborhood of the origin. Finally, abundant simulation experience results demonstrate the effectiveness and robustness of the proposed scheme.

Adaptive Fuzzy Wavelet Neural Network Event-Triggered Consensus Tracking Control for Networked Hyperbolic PDE-ODE Systems

Adaptive Fuzzy Wavelet Neural Network Event-Triggered Consensus Tracking Control for Networked Hyperbolic PDE-ODE Systems

A Fuzzy Wavelet Neural Network (FWNN) and Hybrid Optimization Machine Learning Technique for Traffic Flow Prediction

Traffic go with the flow forecasting is essential in urban planning and management, optimizing transportation structures and resource allocation. However, accurately predicting visitors glide is tough because of its inherent complexity, nonlinearity, and diverse uncertain factors. The trouble declaration underscores the issue in as it should be forecasting site visitors flow, mainly in urban environments characterized through dynamic and complex site visitor’s styles. In the existing paintings there are numerous traditional devices getting to know models used for visitors flow prediction, however those conventional strategies show off barriers in reaching excessive prediction accuracy. Therefore, the proposed work targets to put into effect hybrid optimization techniques for correct prediction in shipping machine. Here fuzzy wavelet neural community (FWNN) is used to address complicated nonlinear structures with uncertain conditions and hybrid optimization method called hybrid firefly and particle swarm optimization (HFO-PSO) which combines the exploration and exploitation talents of firefly and this fusion allows the version to capture intricate visitor’s styles efficiently and optimize the prediction technique, improving accuracy and efficiency. Moreover, the prediction performance of the proposed model is established and compared by means of the usage of distinct measures.

Hybrid approach for 3 phase bridgeless AC to DC Cuk converter for WECS

ABSTRACT This manuscript proposes a novel hybrid method based on a single-stage three-phase bridgeless alternating current (AC) to direct current (DC) Cuk converter for wind energy conversion systems (WECS). The proposed hybrid method is the combination of Mexican Axolotl Optimization (MAO) and Fuzzy Wavelet Neural Network (FWNN), hence it is named as MAO-FWNN method. Moreover, the proposed method enhanced the system performance by distributing the defined reactive-power to harmonic reduction and doubly fed induction generator (DFIG). The proposed converter is modelled under a discontinuous mode of the inductor current. The converter’s output inductor is modelled as a continuous inductor current mode, and it functions to provide enhanced power quality (PQ) and low DC (Direct Current) voltage ripple. At that point, the MATLAB platform is used to execute the performance of the proposed method, and it is compared with existing methods. The performance of the proposed method provides the 0.095 to 0.096 times/sec, which is higher than the existing methods. As a result, the proposed technique is well established compared to the existing techniques.

Multiple Noncooperative Targets Encirclement by Relative Distance-Based Positioning and Neural Antisynchronization Control

From prehistoric encirclement for hunting to GPS orbiting the earth for positioning, target encirclement has numerous real world applications. However, encircling multiple non-cooperative targets in GPS-denied environments remains challenging. In this work, multiple targets encirclement by using a minimum of two tasking agents, is considered where the relative distance measurements between the agents and the targets can be obtained by using onboard sensors. Based on the measurements, the center of all the targets is estimated directly by a fuzzy wavelet neural network (FWNN) and the least squares fit method. Then, a new distributed anti-synchronization controller (DASC) is designed so that the two tasking agents are able to encircle all targets while staying opposite to each other. In particular, the radius of the desired encirclement trajectory can be dynamically determined to avoid potential collisions between the two agents and all targets. Based on the Lyapunov stability analysis method, the convergence proofs of the neural network prediction error, the target-center position estimation error, and the controller error are addressed respectively. Finally, both numerical simulations and UAV flight experiments are conducted to demonstrate the validity of the encirclement algorithms. The flight tests recorded video and other simulation results can be found in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://youtu.be/B8uTorBNrl4</uri>

Design and prediction of self-organizing interval type-2 fuzzy wavelet neural network

This paper presents a self-organizing interval type-2 fuzzy wavelet neural network (SIT2FWNN) model for predicting and identifying nonlinear systems. Based on traditional TSK interval type-2 fuzzy neural network (IT2FNN), the proposed SIT2FWNN utilizes the wavelet basis function with the locating abilities of time-domain and frequency-domain as the consequent of fuzzy rules, combining the capacity of IT2FNN to handle the uncertainty and the great learning potentiality of wavelet neural network (WNN). For the structural adjustment of SIT2FWNN, fuzzy rules are added and deleted according to the Euclidean distance between the input layer and the fuzzification layer. The self-organizing algorithm can delete redundant and unimportant fuzzy rules, thus optimizing the structure of SIT2FWNN and simplifying the calculation. In parameter learning, the AdaBound algorithm and self-adaptive gradient learning algorithm are used to find optimal values of unknown parameters of the SIT2FWNN model. Finally, the designed SIT2FWNN model is applied in the predictions of short-term traffic flow, Mackey-Glass time series, and the opening index of the Shanghai stock index. The evaluation comparison between the proposed model and similar studies proves that SIT2FWNN has higher prediction accuracy and speed.

Fuzzy wavelet neural adaptive finite-time self-triggered fault-tolerant control for a quadrotor unmanned aerial vehicle with scheduled performance

This paper focuses on fuzzy wavelet neural networks-based adaptive finite-time self-triggered fault-tolerant control design with assured tracking performance for a quadrotor unmanned aerial vehicle subject to unknown actuator faults. First, an improved finite-time performance function is integrated with the command-filtered backstepping control framework to assure the scheduled tracking performance, which can effectively constrain the transient fluctuations of the tracking error at fault occurrence. Then, two compensation mechanisms are developed to weaken the adverse impact induced by actuator faults and filter errors. Further, a fuzzy wavelet neural adaptive self-triggered fault-tolerant controller with guaranteed performance is designed to improve the fault insensitivity and tracking accuracy of the controlled vehicle, where the fuzzy wavelet neural networks are employed to approximate the unknown nonlinearities and the control signals are allowed to achieve irregular updating only when the pre-specified trigger protocol is violated. Stability analysis proves that the designed controller guarantees the attitude and position subsystems are practical finite-time stable, and the tracking errors are strictly confined to a preassigned region and never cross its allowed bounds. Finally, the validity of the developed control scheme is confirmed by the simulation results.

Dynamic Analysis and Neural-Adaptive Prescribed-Time Control of the FO Memristive Magnetic-Field Electromechanical Transducer.

This article is concerned with dynamic analysis and neural-adaptive prescribed-time control of the magnetic-field electromechanical transducer incorporating a memristor. First, a fractional-order (FO) mathematical model is developed, which comprehensively characterizes fractional properties of various dielectrics and establishes the relationship between magnetic flux and electric charge. The dynamical analysis explores internal evolution and complexity performance concerning a single factor or double factors among the FO, system parameter, and memristor configuration by the Bifurcation diagram, sample entropy, and C0 complexity from multiple perspectives. Subsequently, a neural-adaptive prescribed-time control scheme is proposed to transform detrimental chaotic oscillations into orderly motions, achieve the pregiven tracking precision and accommodating both actuator fault and system uncertainty. The controller design consists of three key steps: 1) a deferred constraint function is imposed on the tracking error starting from anywhere to get assignable tracking precision within a specified time, ensuring collision avoidance; 2) a type-2 fuzzy wavelet neural network (FWNN) is utilized effectively to handle parameter perturbations and system uncertainties; and 3) a second-order FO tracking differentiator (TD) is utilized to address the "explosion of complexity" of traditional backstepping under actuator fault model. It is shown that the proposed scheme is able to ensure the boundness of all signals of the closed-loop system. Finally, extensive simulation experiments are conducted to validate the effectiveness and robustness of the rendered scheme.

Identification and Robust Control of a Flapping Wing System Using Fuzzy Wavelet Neural Networks

Identification and Robust Control of a Flapping Wing System Using Fuzzy Wavelet Neural Networks

Whale Optimization Algorithm with Fuzzy Wavelet Neural Network for Pneumonia Detection and Classification

Pneumonia detection and classification are a vital medical imaging task that purposes to automatically recognize and classify pneumonia-related abnormalities in chest radiographs or Chest X-Ray (CXR) imaging. Accurate and prior identification of pneumonia is important for suitable treatment and recovering patient results. The main drive is to recognize whether an X-ray image indicates the presence of pneumonia or not. A binary classification techniuqe is trained to distinguish normal and pneumonia X-rays. Deep Learning (DL) approaches are revealed major success in automating this process, assisting healthcare specialists in analyzing pneumonia more effectively. This article presents a Whale Optimization Algorithm with Fuzzy Wavelet Neural Network for Pneumonia Detection and Classification (WOAFWNN-PDC) technique on CXRs. The purpose of the WOAFWNN-PDC technique is to apply optimal DL approaches for the recognition and classification of pneumonia. In the presented WOAFWNN-PDC technique, Gaussian Filtering (GF) approach is used for the noise removal process. In addition, the MobileNetv3 model is utilized for the feature extraction method. Moreover, the FWNN technique was applied to the classification of pneumonia. Finally, the WOA can be executed for an optimum selection of the parameters related to the FWNN approach. The simulation value of the WOAFWNN-PDC algorithm was assessed on a benchmark medical database. The comparative analysis exhibits better results than the WOAFWNN-PDC technique.

Robust hybrid intelligent control using probabilistic feature for active power filter

Harmonic problem in the power grid is becoming increasingly serious, which greatly threatens the stable operation and security of power system. As a reliable solution to suppress harmonics, active power filter (APF) faces with severe challenges in dealing with uncertainties for control design. To tackle this issue, an improved terminal sliding mode control (TSMC) scheme based on modified wavelet fuzzy neural network (MWFNN) is designed for APF. This fills the gap in the application of MWFNN with probabilistic feature in APF system and remedies the flaw that WFNN can't effectively handle the stochastic uncertainties before. Firstly, the dynamic model of APF considering external disturbances and component parameter perturbations is obtained according to its internal structure. Then TSMC is designed for APF, and the stability and finite time convergence are proved by Lyapunov theory. In addition, the MWFNN using probabilistic feature to enhance the advantages of wavelet neural networks is constructed to approximate the uncertain function, and adaptive laws of network parameters are also designed. Finally, compared with recurrent neural network (RNN) and TSMC by simulation and experiment, it can be found that the MWFNN has superior performance in harmonic suppression.Index Terms—Terminal sliding mode control, fuzzy neural network, wavelet fuzzy neural network, active power filter

Fuzzy neural network PID control design of camellia fruit vibration picking manipulator

Due to the growth characteristics of the flowers and fruits of camellia in the same period, the vibrating camellia fruit picking machine needs to ensure the constant rotational speed of the vibrating hydraulic motor when the picking mechanism is operating, to achieve a constant vibration frequency, to ensure that the camellia fruit can smoothly fall off the branches through vibration. In contrast, the camellia fruit does not fall off. In this regard, this paper deduced the state space equation of the camellia fruit picking machine’s valve-controlled vibrating hydraulic motor system and designed a fuzzy wavelet neural network PID controller (FWNN PID controller) based on the traditional incremental PID control principle. Then the designed vibration picking manipulator control system was simulated under no-load, 5 s load conditions, and load start conditions with MATLAB/Simulink, a general PID controller and a fuzzy RBF neural network PID controller (FRBFNN PID controller) were used to contrast with it. The results show that the general PID controller has a slow response speed and poor robustness, while fuzzy neural network PID controllers (including FWNN PID controller and FRBFNN PID controller) have a fast response speed and strong robustness, which can well meet the requirements of a specific vibration frequency. Finally, a field test was carried out. The results show that the FWNN PID control is better than the FRBFNN PID control. Furthermore, the FWNN PID controller obviously reduced the drop rate of camellia flowers within 6% while ensuring the picking efficiency above 90%, which can well meet the needs of the camellia fruit picking operation.

Intelligent Backstepping Control of Permanent Magnet-Assisted Synchronous Reluctance Motor Position Servo Drive with Recurrent Wavelet Fuzzy Neural Network

An intelligent servo drive system for a permanent magnet-assisted synchronous reluctance motor (PMASynRM) that can adapt to the control requirements considering the motor’s nonlinear and time-varying natures is developed in this study. A recurrent wavelet fuzzy neural network (RWFNN) with intelligent backstepping control is proposed to achieve this. In this study, first, a maximum torque per ampere (MTPA) controlled PMASynRM servo drive is introduced. A lookup table (LUT) is created, which is based on finite element analysis (FEA) results by using ANSYS Maxwell-2D dynamic model to determine the current angle command of the MTPA. Next, a backstepping control (BSC) system is created to accurately follow the desired position in the PMASynRM servo drive system while maintaining robust control characteristics. However, designing an efficient BSC for practical applications becomes challenging due to the lack of prior uncertainty information. To overcome this challenge, this study introduces an RWFNN as an approximation for the BSC, aiming to alleviate the limitations of the traditional BSC approach. An enhanced adaptive compensator is also incorporated into the RWFNN to handle potential approximation errors effectively. In addition, to ensure the stability of the RWFNN, the Lyapunov stability method is employed to develop online learning algorithms for the RWFNN and to guarantee its asymptotic stability. The proposed intelligent backstepping control with recurrent wavelet fuzzy neural network (IBSCRWFNN) demonstrates remarkable effectiveness and robustness in controlling the PMASynRM servo drive, as evidenced by the experimental results.

Improvement of Power Quality Using Reduced Switch Count Eleven-Level Inverter in Smart Grid

This article proposed a hybrid control topology to cascaded multilevel-inverter (CMLI) using 11-level for the grid-tied photovoltaic generation system. The proposed hybrid technique combines an archerfish hunting optimiser (AHO) and Spike Neural Network (SNN); hence, it is called the AHO-SNN technique. The proposed control technique is to maintain the regulation of power or maximal energy conversion of the solar subsystem and reduce total harmonic distortion (THD). Here, the AHO is utilised to construct the optimum control signal dataset. The best control signals are estimated using a data set performed by SNN. The resultant control signals will regulate insulated-gate-bi-polar-switches (IGBT) of Cascaded MLI. The proposed AHO-SNN control topology determines the converter switching states by constructing the operating modes of the generation system. The system parameter changes and outside disturbances are optimally minimised with this control strategy. The proposed AHO-SNN control is done in MATLAB platform, and it evaluated their performance by using existing methods, like War Strategy Optimization Algorithm (WSO) Fuzzy Wavelet Neural Network (FWNN), Side-Blotched Lizard Algorithm (SBLA), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Memetic Fire-Fly Algorithm (MFA). The result shows that the proposed approach based on THD is less than existing approaches.

An Efficient Feature Subset Selection with Fuzzy Wavelet Neural Network for Data Mining in Big Data Environment

Big data refers to the massive quality of data being generated at a drastic speed from various heterogeneous sources namely social media, mobile devices, internet transactions, networked devices, and sensors. Several data mining (DM) and machine learning (ML) models have been presented for the extraction of knowledge from Big Data. Since the big datasets include numerous features, feature selection techniques are essential to eliminate unwanted and unrelated features which degrade the classification efficiency. The adoption of DM tools for big data environments necessitates remodeling the algorithm. In this aspect, this paper presents an intelligent feature subset selection with fuzzy wavelet neural network (FSS-FWNN) for big data classification. The FSS-FWNN technique incorporates Hadoop Ecosystem tool for handling big data in an effectual way. Besides, the FSS-FWNN technique involves three processes namely preprocessing, feature selection, and classification. In addition, quasi-oppositional chicken swarm optimization (QOCSO) technique is employed for the feature selection process and the FWNN technique is applied for the classification process. The design of QOCSO algorithm as an FS technique for big data classification shows the novelty of the work and the feature subset selection process considerably enhances the classification performance. An extensive set of simulations is carried out and the results are reviewed in terms of several evaluation factors in order to analyse the improvement of the FSS-FWNN approach. The experimental findings demonstrated that the FSS-FWNN approach outperformed the most current algorithms.

Fuzzy wavelet neural network driven vehicle detection on remote sensing imagery

Remote sensing-based target detection process is applied to spot the targeted objects in remote sensing images (RSIs). However, it is challenging to detect small-sized vehicles in RSIs. The current study designs a Chicken Swarm Optimization with Transfer Learning-Driven Vehicle Detection and Classification on Remote Sensing Imagery (CSOTL-VDCRS) technique to resolve these issues. The presented CSOTL-VDCRS technique employs the mask-region based Convolutional Neural Network (Mask RCNN) technique for the detection of the vehicles. Once the vehicles in the RSIs are detected, the next step is to classify them using the Fuzzy Wavelet Neural Network (FWNN) model. To enhance the performance of the proposed model in terms of vehicle detection, the CSO technique is used as a hyperparameter optimizer for the Mask RCNN technique. The proposed CSOTL-VDCRS technique was experimentally validated using the benchmark dataset, and the outcomes demonstrate its superior performance over other existing models.

Improved fuzzy neural network control for the clamping force of Camellia fruit picking manipulator

During the operation of the vibrating mechanism, the push-shaking camellia fruit picking manipulator needs to ensure a constant force output of the clamping hydraulic motor in order to make sure that the camellia fruit tree trunk wouldn't loosen or damage, which may affect its later growth, during the picking process. In this regard, this paper derived the state space model of the valve-controlled clamping hydraulic motor system of the push-shaking camellia fruit picking manipulator, and the fuzzy wavelet neural network (FWNN) was designed on the basis of the traditional incremental PID control principle and the parameters of the neural network were optimized by the improved grey wolf optimizer (GWO). And then, the control system was simulated with the MATLAB/Simulink software without and with external interference, and compared and analyzed it with traditional PID controller and fuzzy PID (FPID) controller. The results show that the traditional PID controller and the FPID control have slow response and poor robustness, while the improved fuzzy wavelet neural network PID (IFWNN PID) controller possesses the characteristics of fast response and strong robustness, which can well meet the requirement of the constant clamping force of hydraulic motors. Finally, the field clamping test was carried out on the picking manipulator. The results show that the manipulator controlled by the IFWNN PID controller shortens the clamping time by 20.0% and reduces the clamping damage by 13.6% compared with the PID controller, which is verified that the designed controller can meet the clamping operation requirements of the camellia fruit picking machine.

Optimal Fuzzy Wavelet Neural Network Based Road Damage Detection

Floods are one of the most severe and most frequent natural calamities. It causes enormous economic damage and even leads to higher mortality rates. Studies on damage detection of roads using artificial intelligence (AI) methods gained more attention currently, especially in the development of smart cities. Therefore, this study designs an optimal Fuzzy Wavelet Neural Network based Road Damage Detection (OFWNN-RDD) technique for Flooding Management. The OFWNN-RDD technique aims to exploit the remote sensing images to classify different kinds of roads. For noise removal process, the OFWNN-RDD technique utilizes Gabor filtering (GF) technique. In addition, the OFWNN-RDD technique uses the DenseNet121 model for feature vector generation with modified barnacles mating optimization (MBMO) based hyperparameter optimizer. Finally, FWNN image classification approach is used for road damage detection. The simulation values exhibit the supremacy of the OFWNN-RDD technique over other models with improved road damage detection accuracy of 98.56%.

Traffic accident prediction based on an artificial bee colony algorithm and a self-adaptive fuzzy wavelet neural network

Traffic accident prediction based on an artificial bee colony algorithm and a self-adaptive fuzzy wavelet neural network

Adaptive Weiner filtering with AR-GWO based optimized fuzzy wavelet neural network for enhanced speech enhancement.

Speech signal enhancement is a subject of study in which a large number of researchers are working to improve the quality and perceptibility of speech signals. In the existing Kalman Filter method, the short-time magnitude or power spectrum due to random variations of noise was a serious problem and the signal-to-noise ratio was very low. This issue severely reduced the perceived qualityand intelligibility of enhanced speech. Thus, this paper intent to develop an improved speech enhancement model and it includes"training phase and testing phase". In the training phase, the input noise corrupted signal is initially fed as input to both STFT-based noise estimation and NMF-based spectrum estimation forestimating the noise spectrum and signal spectrum, respectively. The obtained noise spectrum and the signal spectrum are fed as input to the Wiener filter and these filtered signals are subjected to Empirical Mean Decomposition (EMD).Since, tuning factor η plays a key role in Wiener filter, it has to be determined for each signal and from the denoised signal the bark frequency is evaluated. The computed bark frequency is fed as input to the learning algorithm referred as Fuzzy Wavelet Neural Network (FW-NN)for detecting the suited tuning factor η for the entire input signal in Wiener filter.An Adaptive Randomized Grey Wolf Optimization (AR-GWO) is proposed for proper tuning of the tuning factor η referred as tuned tuning factor (η tuned ). The proposed AR-GWO is the improved version of standard Grey wolf optimization (GWO). In the testing phase, the training is accomplished initially and from which the tuning factor is gathered for each of the relevant input signal. Then, the properly tuned tuning factor (η tuned ) from FW-NN is fed as input to EMD via adaptive wiener filter for decomposing the spectral signal and the output of EMD is denoised enhanced speech signal. At last, the performance of the adopted approach is evaluated to the existing approaches in terms of various metrics. In particular, the computation time of the adopted AR-GWO model is 34.07%, 43.57%, 28.86%, 38.88%, and 16.03% better than the existing GA, ABC, PSO, FF, and GWO approaches respectively.