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.

scholarly journals Spike discharge prediction based on Neuro-fuzzy system

2017 ◽  
Author(s):  
Mahdi Zarei
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy System ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Subtractive Clustering ◽  
Inference System ◽  
Spike Discharge ◽  
Neuro Fuzzy ◽  
Spike Latency ◽  
Neural Fuzzy

AbstractThis paper presents the development and evaluation of different versions of Neuro-Fuzzy model for prediction of spike discharge patterns. We aim to predict the spike discharge variation using first spike latency and frequency-following interval. In order to study the spike discharge dynamics, we analyzed the Cerebral Cortex data of the cat from [29]. Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Wang and Mendel (WM), Dynamic evolving neural-fuzzy inference system (DENFIS), Hybrid neural Fuzzy Inference System (HyFIS), genetic for lateral tuning and rule selection of linguistic fuzzy system (GFS.LT.RS) and subtractive clustering and fuzzy c-means (SBC) algorithms are applied for data. Among these algorithms, ANFIS and GFS.LT.RS models have better performance. On the other hand, ANFIS and GFS.LT.RS algorithms can be used to predict the spike discharge dynamics as a function of first spike latency and frequency with a higher accuracy compared to other algorithms.

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.

Genetic Algorithmised Neuro Fuzzy System for Forecasting the Online Journal Visitors

2021 ◽  
pp. 181-189
Author(s):  
Wayan Firdaus Mahmudy ◽  
Aji Prasetya Wibawa ◽  
Nadia Roosmalita Sari ◽  
H. Haviluddin ◽  
P. Purnawansyah
Keyword(s):  
Neural Network ◽  
Fuzzy System ◽  
Fuzzy Inference ◽  
Hybrid Approach ◽  
Online Journal ◽  
Inference System ◽  
The Neural Network ◽  
Neuro Fuzzy ◽  
Artificial Neural Network Ann ◽  
Network Weight

Artificial Neural Network (ANN) is recognized as one of effective forecasting engines for various business fields. This approach fits well with non-linear data. In fact, it is a black box system with random weighting, which is hard to train. One way to improve its performance is by hybridizing ANN with other methods. In this paper, a hybrid approach, Genetic Algorithm-Neural Fuzzy System (GA-NFS) is proposed to predict the number of unique visitors of an online journal website. The neural network weight is precisely determined using GA. Afterwards, the best weight has been used for testing data and processed using Sugeno Fuzzy Inference System (FIS) for time-series forecasting. Based on experiment, GA-NFS have been produced accuracy with 0.989 of root mean square error (RMSE) that is lower than the RMSE of a common NFS (2,004). This may indicate that the GA based weighting is able to improve the NFS performance on forecasting the number of journal unique visitors.

scholarly journals Skin Cancer Recognition by Using a Neuro-Fuzzy System

2011 ◽  
Vol 10 ◽  
pp. CIN.S5950 ◽  
Author(s):  
Bareqa Salah ◽  
Mohammad Alshraideh ◽  
Rasha Beidas ◽  
Ferial Hayajneh
Keyword(s):  
Neural Network ◽  
Skin Cancer ◽  
Fuzzy System ◽  
Fuzzy Inference ◽  
Cancer Type ◽  
Inference System ◽  
Mortality And Morbidity ◽  
Neuro Fuzzy ◽  
Neural Network System ◽  
Diagnostic Technologies

Skin cancer is the most prevalent cancer in the light-skinned population and it is generally caused by exposure to ultraviolet light. Early detection of skin cancer has the potential to reduce mortality and morbidity. There are many diagnostic technologies and tests to diagnose skin cancer. However many of these tests are extremely complex and subjective and depend heavily on the experience of the clinician. To obviate these problems, image processing techniques, a neural network system (NN) and a fuzzy inference system were used in this study as promising modalities for detection of different types of skin cancer. The accuracy rate of the diagnosis of skin cancer by using the hierarchal neural network was 90.67% while using neuro-fuzzy system yielded a slightly higher rate of accuracy of 91.26% in diagnosis skin cancer type. The sensitivity of NN in diagnosing skin cancer was 95%, while the specificity was 88%. Skin cancer diagnosis by neuro-fuzzy system achieved sensitivity of 98% and a specificity of 89%.

A GA-Weighted Adaptive Neuro-Fuzzy Model to Predict the Behaviour of Magnetorheological Damper

2014 ◽  
Vol 663 ◽  
pp. 203-207 ◽  
Author(s):  
Mohammadjavad Zeinali ◽  
Saiful Amri Mazlan ◽  
Abdul Yasser Abd Fatah ◽  
Hairi Zamzuri
Keyword(s):  
Neural Network ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Prediction Performance ◽  
Magnetorheological Damper ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Ga Algorithm ◽  
Artificial Neural Network Ann

Magnetorheological damper is a controllable device in semi-active suspension system to absorb unwanted movement. The accuracy of magnetorheological damper model will affect performance of the control system. In this paper, a combination of genetic algorithm (GA) and adaptive-network-based fuzzy inference system (ANFIS) approaches is utilized to model the magnetorheological damper using experimental results. GA algorithm is implemented to modify the weights of the trained ANFIS model. The proposed method is compared with ANFIS and artificial neural network (ANN) methods to evaluate the prediction performance. The result illustrates that the proposed GA-weighted adaptive neuro-fuzzy model has successfully predicted the magnetorheological damper behaviour and outperformed other compared methods.

Structure identification and IO space partitioning in a nonlinear fuzzy system for prediction of patient survival after surgery

2017 ◽  
Vol 10 (2) ◽  
pp. 166-182 ◽  
Author(s):  
Shabia Shabir Khan ◽  
S.M.K. Quadri
Keyword(s):  
Pancreatic Cancer ◽  
Fuzzy Inference System ◽  
Fuzzy System ◽  
Fuzzy Inference ◽  
Patient Survival ◽  
Data Partitioning ◽  
Structure Identification ◽  
Inference System ◽  
Content Type ◽  
Neuro Fuzzy

Purpose As far as the treatment of most complex issues in the design is concerned, approaches based on classical artificial intelligence are inferior compared to the ones based on computational intelligence, particularly this involves dealing with vagueness, multi-objectivity and good amount of possible solutions. In practical applications, computational techniques have given best results and the research in this field is continuously growing. The purpose of this paper is to search for a general and effective intelligent tool for prediction of patient survival after surgery. The present study involves the construction of such intelligent computational models using different configurations, including data partitioning techniques that have been experimentally evaluated by applying them over realistic medical data set for the prediction of survival in pancreatic cancer patients. Design/methodology/approach On the basis of the experiments and research performed over the data belonging to various fields using different intelligent tools, the authors infer that combining or integrating the qualification aspects of fuzzy inference system and quantification aspects of artificial neural network can prove an efficient and better model for prediction. The authors have constructed three soft computing-based adaptive neuro-fuzzy inference system (ANFIS) models with different configurations and data partitioning techniques with an aim to search capable predictive tools that could deal with nonlinear and complex data. After evaluating the models over three shuffles of data (training set, test set and full set), the performances were compared in order to find the best design for prediction of patient survival after surgery. The construction and implementation of models have been performed using MATLAB simulator. Findings On applying the hybrid intelligent neuro-fuzzy models with different configurations, the authors were able to find its advantage in predicting the survival of patients with pancreatic cancer. Experimental results and comparison between the constructed models conclude that ANFIS with Fuzzy C-means (FCM) partitioning model provides better accuracy in predicting the class with lowest mean square error (MSE) value. Apart from MSE value, other evaluation measure values for FCM partitioning prove to be better than the rest of the models. Therefore, the results demonstrate that the model can be applied to other biomedicine and engineering fields dealing with different complex issues related to imprecision and uncertainty. Originality/value The originality of paper includes framework showing two-way flow for fuzzy system construction which is further used by the authors in designing the three simulation models with different configurations, including the partitioning methods for prediction of patient survival after surgery. Several experiments were carried out using different shuffles of data to validate the parameters of the model. The performances of the models were compared using various evaluation measures such as MSE.

Optimized neural network and adaptive neuro-fuzzy controlled dynamic voltage restorer for power quality performance

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Prashant Kumar ◽  
Sabha Raj Arya ◽  
Khyati D. Mistry
Keyword(s):  
Neural Network ◽  
Power Distribution ◽  
Fuzzy Inference ◽  
Dynamic Performance ◽  
Hybrid Approach ◽  
Voltage Regulation ◽  
Dynamic Voltage Restorer ◽  
Inference System ◽  
Intelligence System ◽  
Neuro Fuzzy

Abstract In this article, a hybrid approach is implemented namely, neural network training (NNT) based machine learning (ML) estimator inspired by artificial neural network (ANN) and self-adaptive neuro-fuzzy inference system (ANFIS) to tackle the voltage aggravations in the power distribution network (DN). In this work, potential of swarm intelligence technique namely particle swam optimization (PSO) is analysed to obtain an optimum prediction model with certain modifications in training algorithm parameters. In practice, when the systems are continuously subjected to parametric changes or external disturbances, then ample time is dedicated to tune the system to regain its stable performance. To improve the dynamic performance of the system intelligence-based techniques are proposed to overcome the shortcomings of conventional controllers. So, gain tuning process based on the intelligence system is a desirable choice. The statistical tools are used to proclaim the effectiveness of the controllers. The obtained MSE, RMSE, ME, SD and R were evaluated as 0.0015959, 0.039949, −0.00089838, 0.039941 and 1 in the training phase and 0.0015372, 0.039207, −0.0005657, 0.039203 and 1 in the testing phase, respectively. The results revealed that the ANFIS-PSO network model could accomplish a better DC voltage regulation performance when it is compared to the conventional PI. The proposed intelligence strategies confirm that the predicted DVR model based on NNT-ML and ANFIS has faster convergence speed and reliable prediction rate. Moreover, the simulation results show that the dynamic response is improved with proposed PSO based NNT based ML and ANFIS (Takagi-Sugeno) that significantly compensates the voltage based PQ issues. The proposed DVR is actualized in MATLAB/SIMULINK platform.

Model Identification of 3R Palnar Robot using Neural Network and Adaptive Neuro-Fuzzy Inference System

2021 ◽  
Author(s):  
R. Subasri ◽  
R. Meenakumari ◽  
R. Velnath ◽  
Srinivethaa Pongiannan ◽  
M. Sri Sai Mani Rohit Kumar
Keyword(s):  
Neural Network ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Model Identification ◽  
Inference System ◽  
Neuro Fuzzy
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