The Prediction of the Gas Utilization Ratio based on TS Fuzzy Neural Network and Particle Swarm Optimization

Zhang, Sen; Jiang, Haihe; Yin, Yixin; Xiao, Wendong; Zhao, Biao

doi:10.3390/s18020625

Cited by 36 publications

(12 citation statements)

References 17 publications

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“…Liu et al [20] proposed to use a single fuzzy weight coefficient of a fuzzy neuron for fuzzy control and process learning. In this year, Zhang et al [21] proposed fuzzy neurons with real weight coefficients, fuzzy thresholds, and fuzzy inputs. Panapakidis and Dagoumas [22] proposed a variety of fuzzy neuron models.…”

Section: Fuzzy Neural Network Modelmentioning

confidence: 99%

[Retracted] Foreign Trade Export Forecast Based on Fuzzy Neural Network

Liu

2021

Complexity

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This article first analyzes the significance and methods of foreign trade export forecasting and determines the index system of foreign trade export forecasting by analyzing the results of foreign trade export forecasting research at home and abroad. Subsequently, the related concepts and principles of artificial neural network and fuzzy theory are explained, the types and training algorithms of the fuzzy neural network are introduced, and the neural network and fuzzy theory are combined to establish the prediction model. Finally, according to the characteristics of foreign trade exports, this article comprehensively considers the influence of various factors, applies the fuzzy neural network model to the foreign trade export forecast, introduces the whole process of the establishment of the fuzzy neural network forecasting model in detail, and predicts the change interval of foreign trade exports.

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Section: Fuzzy Neural Network Modelmentioning

confidence: 99%

[Retracted] Foreign Trade Export Forecast Based on Fuzzy Neural Network

Liu

2021

Complexity

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“…At present, four control methods are available to be widely applied in agricultural variable spray and related fields: PID control [38][39][40][41][42][43], fuzzy control [44][45][46][47][48][49][50][51], neural network control, and the corresponding intelligent control [52][53][54][55][56][57][58]. It is a known fact that the PID parameters, once adjusted, can be hard to re-correct.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Multi-Sensor Variable Spray System with Chaotic Optimization and Adaptive Fuzzy Control

Song

Huang

Liang

et al. 2020

Sensors

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During the variable spray process, the micro-flow control is often held back by such problems as low initial sensitivity, large inertia, large hysteresis, nonlinearity as well as the inevitable difficulties in controlling the size of the variable spray droplets. In this paper, a novel intelligent double closed-loop control with chaotic optimization and adaptive fuzzy logic is developed for a multi-sensor based variable spray system, where a Bang-Bang relay controller is used to speed up the system operation, and adaptive fuzzy nonlinear PID is employed to improve the accuracy and stability of the system. With the chaotic optimization of controller parameters, the system is globally optimized in the whole solution space. By applying the proposed double closed-loop control, the variable pressure control system includes the pressure system as the inner closed-loop and the spray volume system as the outer closed-loop. Thus, the maximum amount of spray droplets deposited on the plant surface may be achieved with the minimum medicine usage for plants. Multiple sensors (for example: three pressure sensors and two flow rate sensors) are employed to measure the system states. Simulation results show that the chaotic optimized controller has a rise time of 0.9 s, along with an adjustment time of 1.5 s and a maximum overshoot of 2.67% (in comparison using PID, the rise time is 2.2 s, the adjustment time is 5 s, and the maximum overshoot is 6.0%). The optimized controller parameters are programmed into the hardware to control the established variable spray system. The experimental results show that the optimal spray pressure of the spray system is approximately 0.3 MPa, and the flow rate is approximately 0.08 m3/h. The effective droplet rate is 89.4%, in comparison to 81.3% using the conventional PID control. The proposed chaotically optimized composite controller significantly improved the dynamic performance of the control system, and satisfactory control results are achieved.

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“…There are many methods for data prediction, the traditional methods include multiple linear regression (MLR) [4], support vector machine (SVM) [5], etc., they have been applied to data prediction in many fields. In recent years, as researchers have more and more research on artificial neural network, it has been widely used in data prediction, there are many types of neural networks available for it, for example, back propagation neural network (BPNN) [6], fuzzy neural network (FNN) [7], radial basis function neural network (RBFNN) [8] and general regression neural network (GRNN) [9], etc., compared with traditional prediction methods, artificial neural networks have been proven to have higher prediction accuracy on nonlinear problems [10] [11].…”

Section: Introductionmentioning

confidence: 99%

A Collaborative Compound Neural Network Model for Soil Heavy Metal Content Prediction

Cao

Zhang

2020

IEEE Access

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The prediction of soil heavy metal content is an important part of the management of soil heavy metal pollution, but it is often ignored. At present, there are few studies on the prediction of soil heavy metal content, and it is an urgent problem to choose an efficient method for soil heavy metal content prediction. In this paper, a collaborative compound neural network model (CCNN) was put forward to predict the soil heavy metal content, this model uses wavelet neural network (WNN) as the basic prediction model, and at the same time proposes a parallel bird swarm algorithm (PBSA) to solve the parameter optimization problem of WNN, based on the bird swarm algorithm (BSA), the PBSA not only increases the gathering behavior of individual, but also adopts sine transformation based on fitness difference ratio to carry out the following behavior of beggars to improve the global optimization ability, besides that, the acceptance criterion is used to compare the fitness of individuals after updating to avoid falling into a local optimum. Soil heavy metal content data from Yinchuan city of Ningxia and six new urban areas in Wuhan, China are used to make prediction experiments respectively, through compare with support vector machine (SVM), radial basis function neural network (RBFNN), WNN and bird swarm algorithm optimizes wavelet neural network (BSA-WNN), the experimental results demonstrate that the predicted value of the CCNN is closer to the actual value and has better prediction performance. INDEX TERMS Soil heavy metal content prediction, collaborative compound neural network model, parallel bird swarm algorithm, wavelet neural network.

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The Prediction of the Gas Utilization Ratio based on TS Fuzzy Neural Network and Particle Swarm Optimization

Cited by 36 publications

References 17 publications

[Retracted] Foreign Trade Export Forecast Based on Fuzzy Neural Network

[Retracted] Foreign Trade Export Forecast Based on Fuzzy Neural Network

An Intelligent Multi-Sensor Variable Spray System with Chaotic Optimization and Adaptive Fuzzy Control

A Collaborative Compound Neural Network Model for Soil Heavy Metal Content Prediction

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