Recurrent Fuzzy Neural Network Research Articles

Predicting Surface Ozone Levels in Eastern Croatia: Leveraging Recurrent Fuzzy Neural Networks with Grasshopper Optimization Algorithm

Predicting Surface Ozone Levels in Eastern Croatia: Leveraging Recurrent Fuzzy Neural Networks with Grasshopper Optimization Algorithm

An FPGA-Implemented Antinoise Fuzzy Recurrent Neural Network for Motion Planning of Redundant Robot Manipulators.

When a robot completes end-effector tasks, internal error noises always exist. To resist internal error noises of robots, a novel fuzzy recurrent neural network (FRNN) is proposed, designed, and implemented on field-programmable gated array (FPGA). The implementation is pipeline-based, which guarantees the order of overall operations. The data processing is based on across-clock domain, which is beneficial for computing units' acceleration. Compared with traditional gradient-based neural networks (NNs) and zeroing neural networks (ZNNs), the proposed FRNN has faster convergence rate and higher correctness. Practical experiments on a 3 degree-of-freedom (DOs) planar robot manipulator show that the proposed fuzzy RNN coprocessor needs 496 lookup table random access memories (LUTRAMs), 205.5 block random access memories (BRAMs), 41384 lookup tables (LUTs), and 16743 flip-flops (FFs) of the Xilinx XCZU9EG chip.

Study on the automatic control method for evaporation treatment of wet flue gas desulfurization wastewater in power plants

ABSTRACT DRFNN is used to predict the evaporation state of wastewater, and incremental PID algorithm is combined to adjust the control quantity. By real-time monitoring the state and parameter changes of wastewater evaporation treatment process, online adjusting and optimizing the parameters of DRFNN and incremental PID, the automatic control of desulfurization wastewater evaporation treatment is completed. The experimental results show that this method can achieve more accurate and reliable automatic control, short control time, strong ability to deal with emergencies, and can converge to stability in a short time. This method can effectively reduce the number of early alarms and failures of the system and save maintenance costs. The IAE values of chloride ion (Cl) and mercury (Hg) in this method are 0.3558 and 0.3872, respectively, which proves that the difference between the actual output and the expected output of this method is very small, which proves that the system in this paper has high accuracy.

Degradation prediction of proton exchange membrane fuel cell using a novel neuron-fuzzy model based on light spectrum optimizer

Proton exchange membrane fuel cell (PEMFC) is regarded as the most promising clean energy to address the fossil energy crisis and environmental pollution. However, it is susceptible to frequently variable load and the impurities of hydrogen, which can directly cause the degradation of performance over time during operations. Degradation prediction has received much attention in recent years, as it can improve the durability and reliability of the PEMFC system. This paper proposes an effective multi-step-ahead prediction for PEMFC degradation under various operational conditions by using variational mode decomposition (VMD), a double recurrent fuzzy neural network (DRFNN), and a light spectrum optimizer (LSO). The integrated method enables precise prediction of degradation trends of PEMFC using historical testing data, which brings together their advantages. To better learn degradation trends, VMD is applied to decompose the input voltage signal into a series of sub-signals with a simpler structure. Then, DRFNN with a feedback loop is developed to train each sub-signal model, which can learn and memorize past information. To further enhance the prediction precision of degradation model, LSO is adopted to automatically update the network’s weights. Finally, the prediction performance of the proposed method is experimentally verified under different load conditions. Compared with other degradation methods, the test results reveal that the proposed method can achieve significant improvements in terms of multi-step-ahead prediction accuracy and robustness.

Finite-Time Projective Synchronization of Hyperjerk Systems Modeled With Fuzzy Recurrent Neural Networks

Finite-Time Projective Synchronization of Hyperjerk Systems Modeled With Fuzzy Recurrent Neural Networks

Fixed Time Synchronization of Stochastic Takagi–Sugeno Fuzzy Recurrent Neural Networks with Distributed Delay under Feedback and Adaptive Controls

In this paper, the stochastic Takagi–Sugeno fuzzy recurrent neural networks (STSFRNNS) with distributed delay is established based on the Takagi–Sugeno (TS) model and the fixed time synchronization problem is investigated. In order to synchronize the networks, we design two kinds of controllers: a feedback controller and an adaptive controller. Then, we obtain the synchronization criteria in a fixed time by combining the Lyapunov method and the related inequality theory of the stochastic differential equation and calculate the stabilization time for the STSFRNNS. In addition, to verify the authenticity of the theoretical results, we use MATLABR2023A to carry out numerical simulation.

Depression Classification using Harris Hawk Optimization (HHO) based Recurrent Fuzzy Neural Network (FRNN) for Sentiment Analysis: An Applied Nonlinear Analysis

Depression Classification using Harris Hawk Optimization (HHO) based Recurrent Fuzzy Neural Network (FRNN) for Sentiment Analysis: An Applied Nonlinear Analysis

Enhancing long-term river water quality prediction: Construction and validation of an improved hybrid model

To prevent eutrophication of river caused by excessive inputs of nutrients, accurately predicting these nutrient concentrations is of utmost importance. However, long-term accurate predictions of nutrients concentrations remain a challenge. In this study, an improved hybrid algorithm combining adaptive variational mode decomposition and multi-functional recurrent fuzzy neural network (AVMD-MFRFNN) was proposed. Firstly, whale optimization algorithm (WOA) was developed to identify the optimal number of modes for variational mode decomposition (VMD). Then VMD was employed to decompose input data into intrinsic mode fractions (IMFs), benefiting for reducing the noise effect and non-stationarity of data. Then, MFRFNN with the capability of characterizing complex data was developed to train and test the IMFs. The proposed AVMD-MFRFNN achieved impressive results with the root mean square errors (RMSEs) of 0.036 mg/L, 0.758 mg/L and 1.435 mg/L for predicting total phosphorus (TP), chemical oxygen demand (CODMn) and total nitrogen (TN) after 168 hours. These results signify a decrease of 5.3%, 14.1% and 30.1% compared to the second-best compared method. Moreover, this study demonstrates the potential that integrating the proposed AVMD with other methods can enhance the modeling performance of time series. Furthermore, sensitivity analysis results further validate the effectiveness of WOA in optimizing VMD parameter. Finally, the applicability of AVMD-MFRFNN was confirmed by the data from another river section. These results provide new insights and technical support into the management and preservation of river water environment.

Predefined-Time Synchronization of Multiple Fuzzy Recurrent Neural Networks via a New Scaling Function

Predefined-Time Synchronization of Multiple Fuzzy Recurrent Neural Networks via a New Scaling Function

An Identification Model of Sludge Bulking Based on Self-Organized Recurrent Fuzzy Neural Network

An Identification Model of Sludge Bulking Based on Self-Organized Recurrent Fuzzy Neural Network

Self-Constructing Chebyshev Fuzzy Neural Complementary Sliding Mode Control and Its Application.

In this article, a complementary sliding mode (CSM) controller using a self-constructing Chebyshev fuzzy recurrent neural network (SCCFRNN) is proposed for harmonic suppression control of an active power filter (APF). The SCCFRNN whose structure can be automatically learned through the designed structure self-learning algorithm is introduced to approximate the unknown nonlinear term in the APF dynamic model, so as to improve modeling accuracy and reduce the burden of CSM control (CSMC). The SCCFRNN combines the advantages of a fuzzy neural network (FNN), recurrent neural network (RNN), and Chebyshev neural network (CNN), and all parameters can be adjusted according to the designed adaptive laws. Eventually, through detailed simulation, hardware experiments, and fair comparison, the feasibility and superiority of the proposed control algorithm were verified.

Application of a novel dynamic recurrent fuzzy neural network with rule self-adaptation based on chaotic quantum pigeon-inspired optimization in modeling for gas turbine

With the rapid development of renewable energy, higher requirements are put forward for the control ability of thermal power units. And an accurate system model is the premise and guarantee of controller design. In this article, a novel dynamic recurrent fuzzy neural network with rule self-adaptation modeling approach based on chaotic quantum pigeon-inspired optimization (NDRFNN-CQPIO) is proposed for the modeling of gas turbine system. The introduction of recursive layer, automatic generation of fuzzy rules and fast parameter solving are the main features of the proposed algorithm. First, set the appropriate threshold, and process the system error and distance index through 4 preset cases to generate fuzzy rules. When a new rule is generated, its antecedent parameters are determined at the same time, and define a boundary to determine whether the antecedent parameters need to be adjusted, so as to avoid the generation of redundant Gaussian functions. Then, a pruning technique based on the contributions of the normalized neuron and learning error is raised to trim the redundant fuzzy rules to ensure the model structure more compact and efficient. Finally, using the chaotic quantum pigeon-inspired algorithm to solve the consequent parameters quickly and accurately. The actual operation data of a gas turbine system were selected to identify and verify the model. In the established model, the root mean square error of output power, exhaust gas temperature, rotor speed, and exhaust gas flow are 0.0023, 0.0015, 0.0011 and 0.0069, respectively. The simulation results show that the proposed approach is effective and feasible, and it can be used for the model identification of gas turbine system. Moreover, we conducted robustness analysis of the model and proved that the proposed model has strong robustness, which provides a basis for its further application.

Self-Constructing Fuzzy Neural Fractional-Order Sliding Mode Control of Active Power Filter.

In this article, a fractional-order sliding mode control (FOSMC) scheme is proposed for mitigating harmonic distortions in the power system, whereby a self-constructing recurrent fuzzy neural network (SCRFNN) is used to weaken the effect of compound nonlinearity caused by unknown uncertainties and environmental fluctuations. The fractional-order sliding mode controller (SMC) is constructed to maintain the control system to be asymptotically stable and a fractional-order calculus is introduced into an SMC to soften the sliding manifold design and realize chattering reduction. Considering parameter variations existing in the power system model, SCRFNN is adopted to approximate the unknown dynamics, which is able to dynamically update network structure by optimizing the fuzzy division, and a feedback connection is incorporated into the feedforward neural network, which is regarded as a storage unit to enhance the capability of coping with temporal problem. The control scheme combining the FOSMC with the SCRFNN can make the tracking error and its time derivative converge to zero. Experimental studies demonstrate the validity of the designed scheme, and comprehensive comparisons illustrate its superiority in harmonic suppression and high robustness.

Daily global solar radiation time series prediction using variational mode decomposition combined with multi-functional recurrent fuzzy neural network and quantile regression forests algorithm

Global solar radiation (GSR) prediction capability with a reliable model and high accuracy is crucial for comprehending hydrological and meteorological systems. It is vital for the production of renewable and clean energy. This research aims to evaluate the performance of combined variational mode decomposition (VMD) with a multi-functional recurrent fuzzy neural network (MFRFNN) and quantile regression forests (QRF) models for GSR prediction in daily scales. The hybrid VMD-MFRFNN and QRF models were compared with standalone MFRFNN, random forest (RF), extreme gradient boosting (XGB), and M5 tree (M5T) models across the Lund and Växjö meteorological stations in Sweden. The meteorological data from 2008 to 2017 were used to train the models, while the prediction accuracy was verified by using the data from 2018 to 2021 under five different input combinations. The various meteorological-based scenarios (including the input are air temperatures (Tmin, Tmax, T), wind speed (WS), relative humidity (RH), sunshine duration (SSH), and maximum possible sunshine duration (N)) were considered as input of predictor models. The current study resulted that the M5T model exhibited higher accuracy than RF and XGB models, while the QRF model showed equivalent performance with the M5T model at both study sites. The MFRFNN model outperformed QRF and M5T models across all input combinations at both study sites. The hybrid VMD-MFRFNN model showed the best performance when fewer input variables (Tmin, Tmax, T, WS at Lund station and Tmin, Tmax, T, WS, SSH, RH at Växjö station) were used for GSR prediction. We conclude that the MFRFNN model best predicts average daily GSR when combining all meteorological variables (Tmin, Tmax, T, WS, SSH, RH, N).

Multi-step-ahead stock price prediction using recurrent fuzzy neural network and variational mode decomposition

Financial time series prediction has attracted considerable interest from scholars, and several approaches have been developed. Among them, decomposition-based methods have achieved promising results. Most decomposition-based methods approximate a single function, which is insufficient for obtaining accurate results. Moreover, most existing research has concentrated on one-step-ahead forecasting that prevents market investors from making the best decisions for the future. This study proposes two novel multi-step-ahead stock price prediction methods based on different decomposition techniques, including discrete cosine transform (DCT), i.e., a linear transform, and variational mode decomposition (VMD), i.e., a non-linear transform. DCT-MFRFNN, a method based on DCT and multi-functional recurrent fuzzy neural network (MFRFNN), uses DCT to reduce fluctuations in the time series and simplify its structure and MFRFNN to predict the stock price. VMD-MFRFNN, an approach based on VMD and MFRFNN, brings together their advantages. VMD-MFRFNN consists of two phases. The input signal is decomposed to several intrinsic mode functions (IMFs) using VMD in the decomposition phase. In the prediction phase, each IMF is given to a separate MFRFNN for prediction, and predicted signals are summed to reconstruct the output. DCT-MFRFNN and VMD-MFRFNN use the particle swarm optimization (PSO) algorithm to train MFRFNN. In this research, for the first time, the gradient descent method is used to train MFRFNN. Three financial time series are used to evaluate the proposed methods. Experimental results indicate that VMD-MFRFNN surpasses other state-of-the-art methods. VMD-MFRFNN, on average, shows a decrease of 31.8% in RMSE compared to the MEMD-LSTM method. Also, DCT-MFRFNN outperforms MFRFNN and DCT-LSTM in all experiments, which reveals the favorable effect of DCT on MFRFNN’s performance. To assess the effectiveness of PSO in training VMD-MFRFNN, we compared its performance with twelve different metaheuristic approaches. PSO, on average, shows a decrease of 9.4% in MAPE compared to other metaheuristic methods.

Recurrent general type-2 fuzzy neural networks for nonlinear dynamic systems identification

This paper introduces a recurrent general type-2 Takagi–Sugeno–Kang fuzzy neural network (RGT2-TSKFNN) for the identification of nonlinear systems. In the proposed structure, the general type-2 fuzzy set (GT2FS) and a recurrent fuzzy neural network (RFNN) are combined to obviate the data uncertainties. The fuzzy firing strengths in the developed structure are returned to the network input as internal variables. In the proposed structure, GT2FS is utilized to characterize the antecedent parts while the consequent parts are performed using TSK type. The issues of constructing a RGT2-TSKFNN involve type reduction, structure learning as well as parameter learning. An efficient strategy is developed by utilizing alpha-cuts to decompose a GT2FS into several interval type-2 fuzzy sets (IT2FSs). In order to solve the computation time of the type-reduction issue, a direct defuzzification method is used instead of iterative nature of Karnik–Mendel​ (KM) algorithm. Type-2 fuzzy clustering and Lyapunov criteria are utilized for online structure learning as well as the antecedent and consequent parameters, respectively for reducing the number of rules and guaranteeing the stability of the proposed RGT2-TSKFNN. The reported comparative analysis of the simulation results is utilized to estimate the performance of the proposed RGT2-TSKFNN with respect to other popular type-2 FNNs (T2FNNs) methodologies.

SMS sentiment classification using an evolutionary optimization based fuzzy recurrent neural network.

Sentiment analysis using the inbox message polarity is a challenging task in text mining, this analysis is used to differentiate spam and ham messages in mail. Polarity estimation is mandatory for spam and ham identification, whereas developing a perfect architecture for such classification is the hot demanding topic. To fulfill that, fuzzy based Recurrent Neural network-based Harris Hawk optimization (FRNN-HHO) is introduced, which performs post-classification over the classified messages (spam and ham). Previously the authors tried to classify the spam and ham messages from the collection of SMSs. But sometimes, the spam messages may incorrectly be classified within the ham classes. This misclassification may reduce the accuracy. The sentiment analysis process is performed over the classified messages to improve such classification accuracy. The spam and ham messages from the available data are classified using a Kernel Extreme Learning Machine (KELM) classifier. The sentiment analysis and classification based experimental evaluation is carried out using accuracy, recall, f-measure, precision, RMSE, and MAE. The performance of the proposed architecture is evaluated using threedifferent datasets: SMS, Email, and spam-assassin. The Area under the curve (AUC) of the proposed approach is found to be 0.9699 (SMS dataset), 0.958 (Email dataset), and 0.95 (spam assassin).

Generalized predictive control using improved recurrent fuzzy neural network for a boiler-turbine unit

The ultra supercritical (USC) for the boiler-turbine unit has become an advanced power generation technology due to its high combustion efficiency and low-carbon emission. Considering the complex nonlinearity and uncertainty in USC boiler-turbine units, a novel recurrent fuzzy neural network (RFNN) is produced to model dynamic responses of nonlinear systems and improve the control performance of the outputs in the boiler-turbine unit. However, the number of fuzzy rules in previous networks is mostly predefined with experts’ experience, which contributes to the decline in the generalization and efficiency of system modeling. This challenge is tackled in this paper by a subtractive clustering (SC) algorithm, which can determine the optimal number of fuzzy sets in the proposed RFNN. Additionally, aiming at minimizing the tracking errors of the outputs in the boiler-turbine unit, a global generalized predictive control (GPC) strategy is further designed to control the fuel flow, the feedwater flow, and the steam governor valve in the boiler-turbine unit. With the real-time data generated in a 1000MW USC boiler-turbine unit, the proposed SC-RFNN-based GPC can achieve a testing root mean-square-error (RMSE) of 0.0411 as well as the lower integral absolute error (IAE) values of system outputs.

Intelligent Global Fast Terminal Sliding Mode Control of Active Power Filter

Faced with serious harmonic pollution, a global fast terminal sliding mode control (GFTSMC) based on a novel recurrent fuzzy neural network (NRFNN) strategy for an active power filter (APF) with uncertainty is proposed in this article, which is aimed at improving the power quality and realizing harmonic suppression. First, the GFTSMC is adopted due to its advantages in finite-time convergence and faster convergence rate of tracking error in the system. Second, NRFNN is adopted to approximate the unknown model and lump the uncertainty of the APF system. Because the values of base width, center vector and feedback gain of NRFNN can be adjusted adaptively according to adaptive laws, the accurate approximation of the unknown model can be achieved, and the robustness and accuracy of the APF system can be guaranteed. Finally, the validity and feasibility of the proposed GFTSMC-NRFNN scheme is fully verified by simulation results, showing it has better steady-state and dynamic performance than other existing methods.

IMPROVEMENT OF ADAPTIVE MANAGEMENT BY CONDITION-BASED MAINTENANCE CONCEPT IN THE CEMENT ENTERPRISES

According to reporting of international economic institutions there are risks of economic recession worldwide at present. Actually, there are some challenges for the management process which is a critical in operations of cement enterprises frequently. In view of management there are demanded advanced methods of planning and control of cement enterprises in the cement industry 4.0. In addition to this there are some new trends and requirements for sustainability, environment protection, occupational health and safety, quality and social responsibility. As regards the Holcim Group there are some references for the sustainable construction industry by 2030 and 2050. [6-12, pp.1-2] In case of the adaptive management and operations there are required applications of the advanced IT technologies to improve the Condition-based Maintenance (CBM) in the cement enterprises. This approach will work with application of specific and measurable Key Performance Indicators (KPI) together with online Condition Monitoring Systems (CMS), Computerized Maintenance Management Systems (CMMS), Fuzzy Recurrent Neural Networks’ technologies to improve the prediction process for production and sales, the optimization process for the planning and maintenance accordingly. Therefore, there have been successful proven practical results of application of the above-mentioned techniques and advanced methods of adaptive management in the cement enterprises to achieve the sustainability. Keywords: Condition-based Maintenance (CBM), online Condition Monitoring Systems (CMS), sustainability, adaptive management, Key Performance Indicators (KPI), Environmental, Social and Governance (ESG), Construction and Demolition Waste (CDW), Biodiversity Indicator Reporting System (BIRS), International Union for Conservation of Nature (IUCN), Computerized Maintenance Management Systems (CMMS), Fuzzy Recurrent Neural Network (RNN).