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Water Science and Engineering 2022年第3期15卷 210-217页

作者： Yu-jun Bao Chang-ying Ji Bing Zhang College of Engineering Nanjing Agricultural UniversityNanjing 210031China School of Electrical and Information Engineering Changzhou Institute of TechnologyChangzhou 213032China

Dissolved oxygen(DO)content is an important index of river water quality.Water quality sensors have been used in China for urban river water monitoring and DO content prediction.However,water quality sensors are expensive and difficult to maintain,and have a short operation period and difficult to maintain.This study developed a scientific and accurate method for prediction of DO content changes using fish school features.The behavioral features of the Carassius auratus fish school were described using two-dimensional fish school images.The degree of DO content decline was graded into five levels,and the corresponding numerical ranges of cluster characteristic parameters were determined by considering the opinions of ichthyologists.Finally,the variation of DO content was predicted using the characteristic parameters of the fish school and the multiple-input single-output Takagi-Sugeno fuzzy neural network.The prediction results were basically consistent with the actual variations of DO content.Therefore,it is feasible to use the behavioral features of the fish school to dynamically predict the level of DO content in water,and this method is especially suitable for prediction of sharp decline of DO content in a relatively short time.

关键词： Fish behavior Cluster feature DO fuzzy neural network Water quality monitoring

Design of Automated Opinion Mining Model Using Optimized fuzzy neural network

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Computers, Materials & Continua 2022年第5期71卷 2543-2557页

作者： Ala’A.Eshmawi Hesham Alhumyani Sayed Abdel Khalek Rashid A.Saeed Mahmoud Ragab Romany F.Mansour Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of SciencesTaif UniversityTaif21944Saudi Arabia Centre for Artificial Intelligence in Precision Medicine King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Sentiment analysis or Opinion Mining (OM) has gained significant interest among research communities and entrepreneurs in the recentyears. Likewise, Machine Learning (ML) approaches is one of the interestingresearch domains that are highly helpful and are increasingly applied in severalbusiness domains. In this background, the current research paper focuses onthe design of automated opinion mining model using Deer Hunting Optimization Algorithm (DHOA) with fuzzy neural network (FNN) abbreviatedas DHOA-FNN model. The proposed DHOA-FNN technique involves fourdifferent stages namely, preprocessing, feature extraction, classification, andparameter tuning. In addition to the above, the proposed DHOA-FNN modelhas two stages of feature extraction namely, Glove and N-gram approach.Moreover, FNN model is utilized as a classification model whereas GTOA isused for the optimization of parameters. The novelty of current work is thatthe GTOA is designed to tune the parameters of FNN model. An extensiverange of simulations was carried out on the benchmark dataset and the resultswere examined under diverse measures. The experimental results highlightedthe promising performance of DHOA-FNN model over recent state-of-the-arttechniques with a maximum accuracy of 0.9928.

关键词： Opinion mining sentiment analysis fuzzy neural network metaheuristics feature extraction classification

Modelling of the Slope Solute Loss Based on fuzzy neural network Model

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Computer Systems Science & Engineering 2022年第8期42卷 677-688页

作者： Xiaona Zhang Jie Feng Zhen Hong Xiaona Rui School of Hydrology and Water Resources Nanjing University of Information Science&TechnologyNanjing210044China Water Resources Research Institute China Institute of Water Resources and Hydropower ResearchBeijing100044China Department of Geography and Environmental Sustainability University of OklahomaNorman73071USA Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China

In regards to soil macropores,the solute loss carried by overland flow is a very complex process.In this study,a fuzzy neural network(FNN)model was used to analyze the solute loss on slopes,taking into account the soil macropores.An artificial rainfall simulation experiment was conducted in indoor experimental tanks,and the verification of the model was based on the results.The characteristic scale of the macropores,the rainfall intensity and duration,the slope and the adsorption coefficient of ions,were chosen as the input variables to the Sugeno FNN model.The cumulative solute loss quantity on the slope was adopted as the output variable of the Sugeno FNN model.There were three membership functions,and the type of membership function was gbellmf(generalized bell membership function).The hybrid learning algorithm,which combines the back propagation algorithm with a least square method,was applied to train and optimize the network parameters,and the optimal network parameters were determined.The simulation results showed that the simulated values were consistent with the measured values.

关键词： Slope solute loss soil macropores fuzzy neural network

fuzzy neural network Based Traffic Prediction and Congestion Control in High-Speed networks

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Journal of Computer Science & Technology 2000年第2期15卷 144-149页

作者：费翔何小燕罗军舟吴介一顾冠群 Department of Computer Science and Engineering Southeast University Nanjing P.R. China

Congestion control is one of the key problems in high-speed networks, such as ATM. In this paper, a kind of traffic prediction and preventive congestion control scheme is proposed using neural network approach. Traditional predictor using BP neural network has suffered from long convergence time and dissatisfying error. fuzzy neural network developed in this paper can solve these problems satisfactorily. Simulations show the comparison among no-feedback control scheme, reactive control scheme and neural network based control scheme.

关键词： congestion control neural network fuzzy neural network ATM

fuzzy neural network image filter based on GA

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Journal of Systems Engineering and Electronics 2004年第3期15卷 426-430页

作者：刘涵刘丁李琦 SchoolofAutomation&InformationEngineering Xi'anUniversityofTechnologyXi'an710048P.R.China SchoolofAutomation&InformationEngineering Xi'anUniversityofTechnologyXi'an710048P.R.China

A new nonlinear image filter using fuzzy neural network based on genetic algorithm is proposed. The learning of network parameters is performed by genetic algorithm with the efficient binary encoding scheme. In the following, fuzzy reasoning embedded in the network aims at restoring noisy pixels without degrading the quality of fine details. It is shown by experiments that the filter is very effective in removing impulse noise and significantly outperforms conventional filters.

关键词： genetic algorithm fuzzy neural network image filter impulse noise.

fuzzy neural network FOR MACHINE PARTS RECOGNITION SYSTEM

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Chinese Journal of Mechanical Engineering 2003年第3期16卷 334-336页

作者： Luo XiaobinYin GuofuChen KeHu Xiaobing Luo YangInstitute of CAD&CAM,Sichuan University,Chengdu 610065, China Institute of CAD&CAM Sichuan University Chengdu 610065

The primary purpose is to develop a robust adaptive machine parts recognitionsystem. A fuzzy neural network classifier is proposed for machine parts classifier. It is anefficient modeling method. Through learning, it can approach a random nonlinear function. A fuzzyneural network classifier is presented based on fuzzy mapping model. It is used for machine partsclassification. The experimental system of machine parts classification is introduced. A robustleast square back-propagation (RLSBP) training algorithm which combines robust least square (RLS)with back-propagation (BP) algorithm is put forward. Simulation and experimental results show thatthe learning property of RLSBP is superior to BP.

关键词： fuzzy neural network Image processing RLSBP algorithm Machine partsclassification

A fuzzy neural network Based Dynamic Data Allocation Model on Heterogeneous Multi-GPUs for Large-scale Computations

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International Journal of Automation and computing 2018年第2期15卷 181-193页

作者： Chao-Long Zhang Yuan-Ping Xu Zhi-Jie Xu Jia He Jing Wang Jian-Hua Adu School of Software Engineering Chengdu University of Information Technology School of Computing & Engineering University of Huddersfield School of Computer Science Chengdu University of Information Technology Department of Computing Sheffield Hallam University

The parallel computation capabilities of modern graphics processing units （GPUs） have attracted increasing attention from researchers and engineers who have been conducting high computational throughput studies. However, current single GPU based engineering solutions are often struggling to fulfill their real-time requirements. Thus, the multi-GPU-based approach has become a popular and cost-effective choice for tackling the demands. In those cases, the computational load balancing over multiple GPU ＂nodes＂ is often the key and bottleneck that affect the quality and performance of the real=time system. The existing load balancing approaches are mainly based on the assumption that all GPU nodes in the same computer framework are of equal computational performance, which is often not the case due to cluster design and other legacy issues. This paper presents a novel dynamic load balancing （DLB） model for rapid data division and allocation on heterogeneous GPU nodes based on an innovative fuzzy neural network （FNN）. In this research, a 5-state parameter feedback mechanism defining the overall cluster and node performance is proposed. The corresponding FNN-based DLB model will be capable of monitoring and predicting individual node performance under different workload scenarios. A real=time adaptive scheduler has been devised to reorganize the data inputs to each node when necessary to maintain their runtime computational performance. The devised model has been implemented on two dimensional （2D） discrete wavelet transform （DWT） applications for evaluation. Experiment results show that this DLB model enables a high computational throughput while ensuring real=time and precision requirements from complex computational tasks.

关键词： Heterogeneous GPU cluster dynamic load balancing fuzzy neural network adaptive scheduler discrete wavelet trans-form.

Modeling of grain size in isothermal compression of Ti-6Al-4V alloy using fuzzy neural network

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Rare Metals 2011年第6期30卷 555-564页

作者： LUO Jiao LI Miaoquan School of Materials Science and Engineering Northwestern Polytechnical University Xi＇an 710072 China

Isothermal compression of Ti-6Al-4V alloy was conducted in the deformation temperature range of 1093-1303 K, the strain rates of 0.001, 0.01, 0.1, 1.0, and 10.0 s-1, and the height reductions of 20%-60% with an interval of 10%. After compression, the effect of the processing parameters including deformation temperature, strain rate, and height reduction on the flow stress and the microstructure was investigated. The grain size of primary a phase was measured using an OLYMPUS PMG3 microscope with the quantitative metallography SISC IAS V8.0 image analysis software. A model of grain size in isothermal compression of Ti-6A1-4V alloy was developed using fuzzy neural net- work （FNN） with back-propagation （BP） learning algorithm. The maximum difference and the average difference between the predicted and the experimental grain sizes of primary a phase are 13.31% and 7.62% for the sampled data, and 16.48% and 6.97% for the non-sampled data, respectively. It can be concluded that the present model with high prediction precision can be used to predict the grain size in isothermal compression of Ti-6Al-4V alloy.

关键词： titanium alloy isothermal compression grain size fuzzy neural network

Fault Identification of Internal Combustion Engine based on Support Vector Machine and fuzzy neural network

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International Journal of Plant Engineering and Management 2022年第3期27卷 144-157页

作者： CHEN Decheng HE Xinyu School of Transportation and Vehicle Engineering Shandong University of TechnologyZibo 255000ShandongChina

The internal combustion engine is the main power source of current large⁃scale machinery and equipment.Overhaul and maintenance of its faults are important conditions for ensuring the safe and stable operation of machinery and equipment,and the identification of faults is a prerequisite.Therefore,the fault identification of internal combustion engines is one of the important directions of current research.In order to further improve the accuracy of the fault recognition of internal combustion engines,this paper takes a certain type of internal combustion engine as the research object,and constructs a support vector machine and a fuzzy neural network fault recognition model.The binary tree multi⁃class classification algorithm is used to determine the priority,and then the fuzzy neural network is verified.The feasibility of the model is proved through experiments,which can quickly identify the failure of the internal combustion engine and improve the failure processing efficiency.

关键词： internal combustion engine support vector machine fuzzy neural network fault recognition

A novel compensation-based recurrent fuzzy neural network and its learning algorithm

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Science in China(Series F) 2009年第1期52卷 41-51页

作者： WU Bo WU Ke LU JianHong School of Energy and Environment Southeast University Nanjing 210096 China

Based on detailed study on several kinds of fuzzy neural networks, we propose a novel compensationbased recurrent fuzzy neural network （CRFNN） by adding recurrent element and compensatory element to the conventional fuzzy neural network. Then, we propose a sequential learning method for the structure identification of the CRFNN in order to confirm the fuzzy rules and their correlative parameters effectively. Furthermore, we improve the BP algorithm based on the characteristics of the proposed CRFNN to train the network. By modeling the typical nonlinear systems, we draw the conclusion that the proposed CRFNN has excellent dynamic response and strong learning ability.

关键词： compensation-based recurrent fuzzy neural network sequential learning method improved BP algorithm nonlinear system