Fuzzy Neural Network Algorithm Research Articles

Optuna-DFNN: An Optuna framework driven deep fuzzy neural network for predicting sintering performance in big data

Sintering performance index can reflect the quality of sintered ore, and the level of sintered ore performance directly affects the stability of blast furnace production. Accurate prediction of sinter performance metrics is essential to optimise sinter quality, improve production efficiency and reduce energy consumption. Based on the sintering big data, this paper proposes the Optuna-DFNN model by combining the fuzzy neural network algorithm, the deep neural network algorithm and the Optuna framework to address the problems of difficulty in tuning the parameter, lack of self-learning ability, and poor generalisation of the model of the traditional method when faced with the input of multiple parameters. The paper predicts the four prediction indexes of yield rate, return fines ratio, drum index and RDI+3.15 respectively. Among them, the relative error of the prediction of the yield rate is within 1.3 %, the relative error of the prediction of the return fines ratio is within 2.8 %, the relative error of the prediction of the drum index is within 0.32 %, and the relative error of the prediction of the RDI+3.15 is within 1.4 %. The results indicate that the Optuna-DFNN model has better performance in predicting sintering performance metrics. Based on the Optuna-DFNN sintered ore performance prediction model, the quality of sintered ore can be accurately predicted, the sintering process parameters can be adjusted in a timely manner, the sintering process can be optimized, and the utilization coefficient of sintering can be improved. Provide a reference for the optimisation of production processes, with a positive impact on cost reduction, environmental protection and increased sustainability of production.

Adaptive Control of Underwater Tunnel Monitoring Robot Based on IoT and Fuzzy Neural Network Algorithm

Abstract To improve the navigation ability of underwater tunnel monitoring robots at fixed distances, directions, depths, and heights and to improve the accuracy of tunnel monitoring, an adaptive control method for underwater tunnel monitoring robots based on the Internet of Things (IoT) and fuzzy neural network algorithms is proposed. The structure of underwater tunnel monitoring robots is analyzed based on the IoT, the convolutional neural network algorithm is used to extract the tracking target characteristics of the underwater tunnel monitoring robot, and the obstacle avoidance process of the underwater tunnel monitoring robot is analyzed. The membership degree of the input variable is calculated by the fuzzy control algorithm. The control rule optimizes the neural network algorithm, obtains the target characteristics displayed by the visual tracking of the underwater tunnel monitoring robot based on the fuzzy neural network, uses the adaptive control to estimate the optimal parameters, and finally obtains the adaptive sliding mode control of the underwater tunnel monitoring robot. The experimental results show that the proposed method can accurately realize the target tracking task of the underwater tunnel monitoring robot and has better obstacle avoidance ability.

Fuzzy Neural Network Algorithm Applied to the Construction of a Prediction Model for Online Buying Behavior

Abstract In this paper, we first preprocessed the user’s shopping behavior data, set the prediction goal, constructed the features of the user’s online purchasing behavior prediction model, and classified and selected the constructed features based on the SVM-RFE algorithm. Then, on the basis of the fuzzy neural network algorithm of fuzzy theory, the network purchasing behavior prediction model was constructed by combining the assessment indexes of the prediction model results as well as the 5-fold cross-validation method. Finally, the evaluation results of the prediction model are examined and compared with common prediction algorithms to confirm the performance of the algorithm in this paper. The results show that the average relative error of model training can reach 0.013, and the absolute error with the actual value ranges between [0.01, 0.06]. On the same test set, the F1 value of the prediction model in this paper is between [0.88, 0.91], and the F1 value of the algorithm on each test set has a small difference of only 0.03, and the F1 value of the other prediction models has a maximum difference of 0.09. The prediction model constructed in this paper has a good prediction effect and 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).

Retracted: Variational Fuzzy Neural Network Algorithm for Music Intelligence Marketing Strategy Optimization.

[This retracts the article DOI: 10.1155/2022/9051058.].

Retracted: An Exploratory Research on Constructing a Model of Innovation and Entrepreneurship Education for College Students Based on Fuzzy Neural Network Algorithm

Retracted: An Exploratory Research on Constructing a Model of Innovation and Entrepreneurship Education for College Students Based on Fuzzy Neural Network Algorithm

Retracted: Logistics Distribution Route Optimization Model Based on Recursive Fuzzy Neural Network Algorithm.

[This retracts the article DOI: 10.1155/2021/3338840.].

Retracted: Analysis of College Student Registration Management and Change Prediction Based on Mutated Fuzzy Neural Network Algorithm.

[This retracts the article DOI: 10.1155/2021/7097425.].

Neural network-based speech fuzzy enhancement algorithm for smart home interaction

With the rapid development of artificial intelligence and the continuous improvement of machine learning technology, speech recognition technology is also developing rapidly and the recognition accuracy is improving to meet the higher requirements of people for smart home devices, and combining smart home with voice recognition technology is an inevitable trend for future development. This study aims to propose a speech fuzzy enhancement algorithm based on neural network for smart home interactive speech recognition technology, so the study proposes a combination of fuzzy neural network algorithm (FNN) and stacked self-encoder (SAE) to form SAE-FNN algorithm, which has better non-linear characteristics and can better achieve feature learning, thus improving the performance of the whole system. The results show that with the SAE-FNN algorithm, the maximum relative error absolute value, average relative error and root mean square error are 0.355, 0.063 and 0.978, which are significantly higher than the other two individual algorithms, and the noise of the sound signal has little effect on the SAE-FNN algorithm. Therefore, it can be seen that the proposed SAE-FNN algorithm has excellent noise immunity performance. In summary, it can be seen that this neural network-based speech fuzzy enhancement algorithm for smart home interaction is extremely feasible.

Control of Oxygen Excess Ratio for a PEMFC Air Supply System by Intelligent PID Methods

The hydrogen fuel cell is a quite promising green device, which could be applied in extensive fields. However, as a complex nonlinear system involving a number of subsystems, the fuel cell system requires multiple variables to be effectively controlled. Oxygen excess ratio (OER) is the key indicator to be controlled to avoid oxygen starvation, which may result in severe performance degradation and life shortage of the fuel cell stack. In this paper, a nonlinear air supply system model integrated with the fuel cell stack voltage model is first built, based on physical laws and empirical data; then, conventional proportional-integral-derivative (PID) controls for the oxygen excess ratio are implemented. On this basis, fuzzy logic inference and neural network algorithm are integrated into the conventional PID controller to tune the gain coefficients, respectively. The simulation results verify that the fuzzy PID controller with seven subsets could clearly improve the dynamic responses of the fuel cells in both constant and variable OER controls, with small overshoots and the fastest settling times of less than 0.2 s.

Research on dynamic path planning method of electric inspection robot based on fuzzy neural network

In order to better address the issue of the coverage rate about power inspection robot to the working area, solving the difficulties in the navigation modeling and path planning of the intelligent control program is the focus of this paper. This paper first analyzes a multi-information fusion SLAM robot positioning and navigation algorithm, then uses the Kalman filter technology to locate through Voronoi mapping. Second, this paper proposes a five-layer neural network approach to how robots can plan and control routes in high-density scenarios. Third, the robot takes the surrounding obstacle distance, the target angle and the target distance as input parameters then the output parameters of the fuzzy neural network are the wheel speed and the steering angle. The inspection robot uses the sensors on the body to detect the surrounding environment’s parameter information. The dynamic path planning algorithm changes the path accordingly, prevents collision, and finally reaches the preset target. Finally, the paper collects the features obtained by Voronoi mapping in a large-scale simulation environment. The fuzzy neural network algorithm for detecting robots is validated in a complex environment, proving the effectiveness and accuracy of the algorithm.

Prediction of English Major Training Model in Higher Vocational Education Based on Fuzzy Neural Network Algorithm

Prediction of English Major Training Model in Higher Vocational Education Based on Fuzzy Neural Network Algorithm

Data-Driven Model Predictive Control-Based Proactive Scheduling for Commercial Microgrid Considering Anomaly Detection

One of the challenging tasks for commercial microgrids with distributed generation is to ensure critical loads resilience during high-impact, low-frequency extreme events. This article proposes a data-driven model predictive control-based proactive scheduling strategy for microgrids subject to extreme weather events. The goal is to maximize the utility value of the building by realizing the difference between the comfort level of critical loads and the total operating cost of the commercial microgrid during the planning horizon. A two-layer framework is proposed to solve the considered problem. First, the aggregate demand load measured by smart meters is processed using a combined long short-term memory and fuzzy neural network algorithm to eliminate anomaly data. Then, the obtained real data are applied through the deep-Q network method to seek appropriate proactive scheduling solutions. However, the rewards obtained from the deep-Q network method are random because of the uncertainties in extreme events. Therefore, robust optimization namely, robust deep reinforcement learning, is incorporated to enhance the robustness of the solutions. The proposed framework ensures the optimality and feasibility of the commercial microgrid's proactive scheduling strategy within the uncertainties in extreme events and the different intensity levels of anomalous data. Extensive simulation results prove the effectiveness of the proposed optimization model and algorithm for enhancing commercial microgrid resilience in extreme events. Furthermore, it also shows good ability in detecting smart meters anomalous data, with a stable convergence value of the performance index, to secure commercial microgrid smart meter data.

Image Enhancement by Using Fuzzy Firefly Optimization and Fuzzy Perceptron Neural Network

The image enhancement methods play an important role in digital image processing. And by using a different kinds of image enhancement techniques, such as artificial intelligence techniques methods. The aim of this methods is to enhance the visual appearance of the digital image and to reduce image noise. In this paper, to enhance the corrupted image and de-noise image, we used swarm optimization algorithms such as a firefly algorithm (FA) and also used neural network such as the perceptron neural network algorithm (PNN). And then after we added the fuzzy membership function to these two algorithms, we obtained to a new method called a fuzzy firefly algorithm (FFA) and fuzzy perceptron neural network algorithm (FPNN). And was computed the performance and efficiency measures for all methods, such as RMSE and PSNR. And the FFA method was the best among the other methods used in this paper.

Integrated photovoltaic storage joint smoothing strategy based on simultaneous perturbation stochastic approximation algorithm

In order to realize the real-time control of photovoltaic power generation smoothly connected to the grid under the condition that the energy storage equipment can operate safely, a control strategy combining the simultaneous perturbation stochastic approximation (SPSA) algorithm with rule control is designed. Firstly, the photovoltaic data are processed to extract the data characteristics of the power ramp, and then the grid-connected reference power is obtained through SPSA algorithm. Secondly, considering the state of charge (SOC) of energy storage equipment and the safe operating power range of hydrogen storage equipment, 24 hybrid energy storage power allocation rules are formulated by using the rule control method. Finally, according to the sampling data of every 10 s interval in typical day, the simulation is carried out on MATLAB/simulink platform. The results show that, compared with the first-order low-pass filtering algorithm and recursive fuzzy neural network (RFNN) algorithm, the SPSA algorithm has stronger smoothing ability, and the rule control can also complete the allocation according to the characteristics of the hybrid storage device while ensuring its normal operation.

Fuzzy Neural Network Algorithm Application in User Behavior Portrait Construction

Fuzzy Neural Network Algorithm Application in User Behavior Portrait Construction

Review of Intelligent Control Systems with Robotics

Interactive between human and robot assumes a significant job in improving the productivity of the instrument in mechanical technology. Numerous intricate undertakings are cultivated continuously via self-sufficient versatile robots. Current automated control frameworks have upset the creation business, making them very adaptable and simple to utilize. This paper examines current and up and coming sorts of control frameworks and their execution in mechanical technology, and the job of AI in apply autonomy. It additionally expects to reveal insight into the different issues around the control frameworks and the various approaches to fix them. It additionally proposes the basics of apply autonomy control frameworks and various kinds of mechanical technology control frameworks. Each kind of control framework has its upsides and downsides which are talked about in this paper. Another kind of robot control framework that upgrades and difficulties the pursuit stage is man-made brainpower. A portion of the speculations utilized in man-made reasoning, for example, Artificial Intelligence (AI) such as fuzzy logic, neural network and genetic algorithm, are itemized in this paper. At long last, a portion of the joint efforts between mechanical autonomy, people, and innovation were referenced. Human coordinated effort, for example, Kinect signal acknowledgment utilized in games and versatile upper-arm-based robots utilized in the clinical field for individuals with inabilities. Later on, it is normal that the significance of different sensors will build, accordingly expanding the knowledge and activity of the robot in a modern domain

Intelligent Construction Optimization Control of Construction Project Schedule Based on the Fuzzy Logic Neural Network Algorithm

At present, the field of construction engineering is limited by various situations, such as complex construction environments and many uncertain factors. Therefore, on the basis of the engineering network diagram, this paper proposes a construction project schedule management method based on the fuzzy logic neural network algorithm. By building a neural network, a large amount of historical data is input, and computers are allowed to calculate the key routes, thus predicting the construction period, and a construction project in a city is taken as an example for simulation experiments. The traditional construction period management scheme expects a construction period of 55 days. The planned construction period optimized by the project management technology integrating fuzzy logic neural network algorithm is 55 days, which is 2 days less than the traditional construction project schedule management technology and will not cause construction period delay. The simulation results show that this algorithm is more accurate and more efficient in calculating the key lines when dealing with large-scale projects, which can help the construction unit to quickly find the optimal strategy and effectively reduce the construction delay and capital loss caused by uncertainty factors.

Children's Motor Intelligence Evaluation System Based on Multi-data Fusion.

Along with the main feedback information of early childhood education activities, children's movement evaluation plays an important guiding role in early childhood education activities. Teachers can teach children in accordance with their aptitude according to the results of children's sports evaluation. Traditional physical education overemphasizes the leading role of teachers, and young children can only passively receive the education of teachers. Teachers always use the same standard to evaluate each child, simply assigning a rating of “strong,” “moderate,” and “poor” to children, ignoring the differences in children's physical intelligence. The emergence of multidata fusion technology can use the differences and complementarity of various data in evaluation functions to make up for the insufficiency of a single exercise evaluation result, purposefully choose the evaluation method flexibly according to the content of the exercise and the scene of the exercise, and gradually improve the level of children's exercise evaluation. This paper studied the children's motor intelligence evaluation system based on multidata fusion. To this end, this paper will focus on children's autonomous sports games and determine the first-level indicators in the children's sports evaluation index system as four indicators: classroom performance, physical fitness, motor skills, and extracurricular fitness. It used the principle of multidata fusion, firstly evaluates children's exercise physiology data through a fuzzy neural network algorithm, and then combines the adaptive weighted data fusion algorithm with D-S evidence theory to evaluate children's movement intelligence. Experiments showed that the multidata evaluation system can take effective measures to intelligently evaluate children's comprehensive motor ability. Compared with the traditional evaluation method, the evaluation results are increased by 5%, and the children's sports evaluation results are more average, which can enhance children's sports confidence and promote children's effective exercise.

Integration of ARIMA and LSTM Models for Remaining Useful Life Prediction of a Water Hydraulic High-Speed On/Off Valve

Some water hydraulic manipulators used for the remote operation of reactors are controlled by a high-speed on/off valve (HSV). Water hydraulic HSVs operate through a process of high-frequency switching, and since their work environment is poorly lubricated, their components are prone to failure. The present study proposed a hybrid model to detect the state and predict the RUL of water hydraulic HSVs used for manipulators, including (1) an HSV state detection method based on the fuzzy neural network (FNN) algorithm; (2) a remaining useful life (RUL) prediction method based on the integration between the autoregressive integrated moving average (ARIMA) model and the long short-term memory (LSTM) model. Final results showed that the accuracy of state detection based on the FNN method was 93.3%. The relative error of the RUL prediction based on the ARIMA–LSTM was less than 1.6%. The developed method can provide guidance for operation and maintenance personnel to plan maintenance reasonably.