Comparative analysis of performance of neural network and neuro-fuzzy model in prediction of groundwater table fluctuation

2012 ◽  
Vol 2 (3) ◽  
pp. 252 ◽  
Author(s):  
P.R. Maiti ◽  
Medha Jha ◽  
Sabita Madhvi Singh
Keyword(s):  
Neural Network ◽  
Comparative Analysis ◽  
Fuzzy Model ◽  
Groundwater Table ◽  
Neuro Fuzzy ◽  
Groundwater Table Fluctuation ◽  
Analysis Of Performance

scholarly journals Soft Computing Tool Approach for Texture Classification Using Discrete Cosine Transform

2011 ◽  
pp. 12-16
Author(s):  
Pankaj H. Chandankhede
Keyword(s):  
Neural Network ◽  
Soft Computing ◽  
Learning Algorithm ◽  
Texture Classification ◽  
Fuzzy Model ◽  
Performance Comparison ◽  
Cosine Transform ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Computing Models

Texture can be considered as a repeating pattern of local variation of pixel intensities. Cosine Transform (DCT) coefficients of texture images. As DCT works on gray level images, the color scheme of each image is transformed into gray levels. For classifying the images using DCT, two popular soft computing techniques namely neurocomputing and neuro-fuzzy computing are used. A feedforward neural network is used to train the backpropagation learning algorithm and an evolving fuzzy neural network to classify the textures. The soft computing models were trained using 80% of the texture data and the remaining was used for testing and validation purposes. A performance comparison was made among the soft computing models for the texture classification problem. In texture classification the goal is to assign an unknown sample image to a set of known texture classes. It is observed that the proposed neuro-fuzzy model performed better than the neural network.

scholarly journals Penggunaan ANFIS pada Pengaturan Debit Air Berdasarkan Volume Air Dalam Tangki

2020 ◽  
Vol 1 (1) ◽  
pp. 24-32
Author(s):  
Machrus Ali ◽  
Ruslan Hidayat ◽  
Iwan Cahyono
Keyword(s):  
Neural Network ◽  
Water Level ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Ball Valve ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Output Flow

Adaptive Neuro-Fuzzy Inference System (ANFIS) adalah penggabungan mekanisme Fuzzy Inference System (FIS) dan Neural Network (NN) yang digambarkan dalam arsitektur jaringan syaraf. Sistem inference fuzzy yang digunakan adalah sistem inference fuzzy model Tagaki-Sugeno-Kang (TSK) orde satu dengan pertimbangan kesederhanaan dan kemudahan komputasi. Pada penelitian ini sebagai pembanding didesain tanpa control, desain dengan PID standart, desain dengan Fuzzy Login Controller (FLC), dan ANFIS controller. Dalam desain penelitian ini yang dikontrol adalah ball valve electric pada tangki agar debit air yang keluar dari tangki sesuai dengan yang dibutuhkan dalam proses produksi dengan menggunakan empat control. Dari simulasi diapatkan bahwa Dsain Water Level yang paling baik pada percobaan ini adalah menggunakan metode ANFIS dengan nilai overshot dan undershot terkecil pada water level dan output flow. Sehingga desain ini bias dipakai acuan untuk menghasilkan control aliran air sesuai dengan harapan yang diinginkan. Hasil simulasi ini akan dibandingkan lagi dengan metode kecerdasan buatan yang lain, sehingga adan didapatkan hasil yang paling sesuai.

scholarly journals Optimization of parameters of neuro-fuzzy model

2020 ◽  
Vol 19 (1) ◽  
pp. 229
Author(s):  
Lyalya Bakievna Khuzyatova ◽  
Lenar Ajratovich Galiullin
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Fuzzy Model ◽  
Network Models ◽  
Knowledge Bases ◽  
Fuzzy Neural Networks ◽  
Neural Network Models ◽  
Neuro Fuzzy ◽  
Fuzzy Neural

<p>The need for increasing the efficiency of the neuron-fuzzy model in the formation of knowledge bases is being updated. The task is to develop methods and algorithms for presetting and optimizing the parameters of a fuzzy neural network. To solve difficult formalized tasks, it is necessary to develop decision support systems - expert systems based on a knowledge base. ES developers are constantly faced with the problems of “extraction” and formalization of knowledge, as well as the search for new ways to obtain it. To do this, use the extraction, acquisition and formation of knowledge. Currently, the formation of knowledge bases is relevant for the creation of hybrid technologies - fuzzy neural networks that combine the advantages of neural network models and fuzzy systems. The analysis of the efficiency of the fuzzy neural network carried out in the work showed that the quality of training of the NN largely depends on the choice of the number of fuzzy granules for input drugs. In addition, to use fuzzy information formalized by the mathematical apparatus of fuzzy logic, procedures are required for selecting optimal forms and presetting the parameters of the corresponding membership functions (MF).</p>

Prediction Model in Electrodialysis Process Based on ANFIS

2011 ◽  
Vol 268-270 ◽  
pp. 332-335
Author(s):  
Guo Lin Jing ◽  
Wen Ting Du ◽  
Xiang Chen ◽  
Huan Yi
Keyword(s):  
Neural Network ◽  
Flow Rate ◽  
Fuzzy System ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Nacl Solution ◽  
Direct Proportion ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Predict Model

Fuzzy system is known to predict model in the electrodialysis process. This paper aimed to predict separation percent(SP) of NaCl solution as a function of concentration, temperature, flow rate and voltage. Besides, in the MATLAB, ANFIS(Adaptive Neuro-Fuzzy Inference System) based on Sugeno fuzzy model, its structure was similar to neural network and could generate fuzzy rules automatically. We obtained fitted values of SP by ANFIS. Then, we studied these influencing factors on fitted values of SP. Finally, we draw a conclusion that SP is in direct proportion to temperature and voltage, but in inverse proportion to concentration and flow rate.

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