The Intelligent Diagnostic System of Vegetable Diseases Based on a Fuzzy Neural Network

2013 ◽  
Vol 321-324 ◽  
pp. 1907-1911 ◽  
Author(s):  
Qing Feng Wei ◽  
Chang Shou Luo ◽  
Cheng Zhong Cao ◽  
Qiang Guo
Keyword(s):  
Neural Network ◽  
Plant Protection ◽  
Back Propagation ◽  
Diagnostic System ◽  
Fuzzy Rule ◽  
Disease Diagnosis ◽  
Back Propagation Neural Network ◽  
Diagnostic Model ◽  
Mixed Programming ◽  
Fuzzy Neural

To overcome the disadvantages that image analysis of agricultural disease diagnosis was not practical in the field, and the expert diagnosis system had an unsatisfied correct rate, a diagnostic model based on fuzzy rule and BP neural network (back propagation neural network) was constructed. The input vector in the model was formed by a unified description of symptoms using plant protection terms and combined with the membership. The intelligent diagnostic system of vegetable diseases based on the diagnostic model was developed by the mixed programming of Visual C # and Matlab. The test shows that the diagnostic correct rate of the system is 88.95%, and it has better fault tolerance and practical value.

ARRHYTHMIA DISEASE DIAGNOSIS USING NEURAL NETWORK, SVM, AND GENETIC ALGORITHM-OPTIMIZED k-MEANS CLUSTERING

2011 ◽  
Vol 11 (04) ◽  
pp. 897-915 ◽  
Author(s):  
ROSHAN JOY MARTIS ◽  
CHANDAN CHAKRABORTY
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Normal Sinus Rhythm ◽  
Optimization Technique ◽  
Back Propagation ◽  
Principal Component ◽  
Disease Diagnosis ◽  
Back Propagation Neural Network ◽  
Polynomial Kernel ◽  
Support Vector

This work aims at presenting a methodology for electrocardiogram (ECG)-based arrhythmia disease detection using genetic algorithm (GA)-optimized k-means clustering. The open-source ECG data from MIT-BIH arrhythmia database and MIT-BIH normal sinus rhythm database are subjected to a sequence of steps including segmentation using R-point detection, extraction of features using principal component analysis (PCA), and pattern classification. Here, the classical classifiers viz., k-means clustering, error back propagation neural network (EBPNN), and support vector machine (SVM) have been initially attempted and subsequently m-fold (m = 3) cross validation is used to reduce the bias during training of the classifier. The average classification accuracy is computed as the average over all the three folds. It is observed that EBPNN and SVM with different order polynomial kernel provide significant accuracies in comparison with k-means one. In fact, the parameters (centroids) of k-means algorithm are locally optimized by minimizing its objective function. In order to overcome this limitation, a global optimization technique viz., GA is suggested here and implemented to find more robust parameters of k-means clustering. Finally, it is shown that GA-optimized k-means algorithm enhances its accuracy to those of other classifiers. The results are discussed and compared. It is concluded that the GA-optimized k-means algorithm is an alternate approach for classification whose accuracy will be near to that of supervised (viz., EBPNN and SVM) classifiers.

Recognizing Odor Mixtures Using Optimized Fuzzy Neural Network Through Genetic Algorithms

2005 ◽  
Vol 9 (3) ◽  
pp. 290-296 ◽  
Author(s):  
Benyamin Kusumoputro ◽  
◽  
Teguh P. Arsyad
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Multilayer Perceptron ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Recognition System ◽  
Back Propagation Neural Network ◽  
Odor Recognition ◽  
Fuzzy Neural ◽  
Odor Mixtures

Recognizing odor mixtures is rather difficult in artificial odor recognition system, especially when the number of sensors is limited. Classification is further hampered if the number of unlearned odor mixtures classes is increased. We developed a fuzzy-neuro multilayer perceptron as a pattern classifier and compared its recognition with that of the Probabilistic Neural Network and Back-propagation Neural Network. To enhance the recognition capability of the system, we then optimized fuzzy-neuro multilayer perceptron topology by deleting its weak weight connections using Genetic Algorithms. Experimental results show that the optimized fuzzy-neuro multilayer perceptron has the highest recognition in 18 classes of two-mixture odors with almost 98.2% when using hardware with 16 sensors, compared to 83.3% when using 8 sensors.

Passenger comfort evaluation of the stowing and unloading luggage task in high-speed trains: Fusion of R-DEMATEL and FNN

2021 ◽  
pp. 1-13
Author(s):  
Hang Zhao ◽  
Jianjie Chu ◽  
Rong Mo ◽  
Chen Chen ◽  
Ning Ding
Keyword(s):  
Neural Network ◽  
High Speed ◽  
Evaluation Model ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Passenger Comfort ◽  
Evaluation Indexes ◽  
Comfort Evaluation ◽  
Fuzzy Neural ◽  
High Speed Trains

At present, high-speed trains have become popular modern transportation. As a significant part of the high-speed train riding activity, the stowing and unloading luggage task has its characteristics. To comprehensively and reasonably evaluate passenger comfort of the stowing and unloading luggage task in high-speed trains. In this paper, passenger behavior characteristics are firstly analyzed by the author, the theoretical architecture of passenger comfort evaluation is constructed with the perspective of product aesthetics and ergonomics, and then the process of the passenger comfort evaluation is put forward. Secondly, a combination of Rough Number (RN) and Decision Making Trial and Evaluation Laboratory (DEMATEL) (i.e. R-DEMATEL) is utilized to solve the centrality degree of comfort influencing factors and determine comfort evaluation indexes. Furthermore, the passenger comfort evaluation model with Fuzzy Neural Network (FNN) is constructed and trained. After that, the sample data of the evaluation are collected through the simulated experiment of the stowing and unloading luggage task, and they are trained with FNN comparing to Back Propagation Neural Network (BPNN). Eventually, the result of examples testing is verified that the effectiveness of the proposed method.

Cost Estimation of Construction Project Using Fuzzy Neural Network Model Embedded with Modified Particle Optimizer

2011 ◽  
Vol 243-249 ◽  
pp. 6296-6301 ◽  
Author(s):  
Xin He ◽  
Yong Yao ◽  
Hong Wen Xia ◽  
You Wen Su ◽  
Gang Liu
Keyword(s):  
Neural Network ◽  
Cost Estimation ◽  
Construction Projects ◽  
Fuzzy Neural Network ◽  
Construction Project ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Particle Swarm Optimizer ◽  
Fuzzy Neural ◽  
Improved Model

A fuzzy neural network (FNN) model embedded with a modified particle swarm optimizer (MPSO) is proposed in this study for cost estimation of construction projects. The proposed method has advantages over traditional FNN approaches in ways of generalization ability and automatic parameter optimization. Comparative studies show that this improved model is also superior to those of BP (back propagation) neural network, PSO-BP and PSO-FNN. Effectively improved accuracy of the developed model in cost estimation of construction project was discussed in case studies. This study provides a base for decision-making in the management of construction project.

scholarly journals Improving Dam Seepage Prediction Using Back-Propagation Neural Network and Genetic Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Xuan Zhang ◽  
Xudong Chen ◽  
Junjie Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Statistical Model ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Diagnostic Model ◽  
Nonlinear Relationship ◽  
Flood Season ◽  
Seepage Model ◽  
Near Future

Statistical model is a traditional safety diagnostic model for dam seepage. It can hardly display the nonlinear relationship between dam seepage and the load sets and has the disadvantage of poor extension prediction. In this paper, the theories of Back Propagation Neural Network (BPNN) combined with Genetic Algorithm (GA) are applied to the seepage prediction model. Taking a typical dam in China as an example, the prediction results of BPNN-GA model and statistical model are compared with the monitoring values. The results show that the improved dam seepage model enhances the ability of nonlinear mapping and generalization and makes the seepage prediction more accurate and reasonable in the near future. According to the established criterion, the safety state of the dam in flood season is evaluated.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

An Anatomy of a Hybrid Color Descriptor with a Neural Network Model to Enhance the Retrieval Accuracy of an Image Retrieval System

2019 ◽  
Vol 13 ◽  
Author(s):  
Shikha Bhardwaj ◽  
Gitanjali Pandove ◽  
Pawan Kumar Dahiya
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Retrieval ◽  
Hybrid System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Retrieval Accuracy ◽  
Color Descriptor ◽  
Benchmark Datasets ◽  
Color Moment

Background: In order to retrieve a particular image from vast repository of images, an efficient system is required and such an eminent system is well-known by the name Content-based image retrieval (CBIR) system. Color is indeed an important attribute of an image and the proposed system consist of a hybrid color descriptor which is used for color feature extraction. Deep learning, has gained a prominent importance in the current era. So, the performance of this fusion based color descriptor is also analyzed in the presence of Deep learning classifiers. Method: This paper describes a comparative experimental analysis on various color descriptors and the best two are chosen to form an efficient color based hybrid system denoted as combined color moment-color autocorrelogram (Co-CMCAC). Then, to increase the retrieval accuracy of the hybrid system, a Cascade forward back propagation neural network (CFBPNN) is used. The classification accuracy obtained by using CFBPNN is also compared to Patternnet neural network. Results: The results of the hybrid color descriptor depict that the proposed system has superior results of the order of 95.4%, 88.2%, 84.4% and 96.05% on Corel-1K, Corel-5K, Corel-10K and Oxford flower benchmark datasets respectively as compared to many state-of-the-art related techniques. Conclusion: This paper depict an experimental and analytical analysis on different color feature descriptors namely, Color moment (CM), Color auto-correlogram (CAC), Color histogram (CH), Color coherence vector (CCV) and Dominant color descriptor (DCD). The proposed hybrid color descriptor (Co-CMCAC) is utilized for the withdrawal of color features with Cascade forward back propagation neural network (CFBPNN) is used as a classifier on four benchmark datasets namely Corel-1K, Corel-5K and Corel-10K and Oxford flower.

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