MODELING PNEUMATIC MUSCLE ACTUATORS: ARTIFICIAL INTELLIGENCE APPROACH

2010 ◽  
Vol 07 (02) ◽  
pp. 151-164 ◽  
Author(s):  
PRASHANT JAMWAL ◽  
S. Q. XIE ◽  
SHAHID HUSSAIN ◽  
KEAN AW
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy System ◽  
Fuzzy Inference ◽  
Human Interaction ◽  
Time Dependent ◽  
Light Weight ◽  
Inference System ◽  
Pneumatic Muscle ◽  
Dependent Behavior ◽  
Muscle Actuators

Robot human interaction requires use of safe, compliant and light weight actuators. Conventional linear motors and pneumatic cylinders are normally used to actuate robots to assist and augment human motions. Lately it has been realized that these actuators are not suitable and safe for applications involving human actor. Their large weight, size and stiffer design raise concerns. Pneumatic muscle actuators (PMA) on the other hand are very light weight, compact and have inherent compliance which make them potential candidate for applications involving robot human interaction. Taking on the advantages, these actuators are now being experimented for a variety of medical and rehabilitation applications. However they are not very popular due to their highly nonlinear and time dependent behavior which poses control problems. In this paper, an attempt is being made to accurately predict the uncertain and ambiguous characteristics of PMA using Artificial Intelligence (AI). Conventional tools such as analytical and numerical methods can only model a nonlinear system which is time independent. Time varying nonlinear system characteristics can be best modeled using artificial intelligence-based regression models. In this research, Artificial Neural Network (ANN), Mamdani Fuzzy Inference System (FIS) and Takagi-Sugeno (TS)-based fuzzy system are developed after carefully analyzing the time series data obtained from a real system. To achieve higher accuracy from these models, their parameters are tuned. Parameters of ANN are tuned using back propagation algorithm whereas fuzzy parameters are tuned using three different methods, namely, gradient descent method (GD), genetic algorithms (GA) and Modified Genetic Algorithm (MGA). It was found that the TS fuzzy inference system tuned by MGA provides better accuracy and can also model the time dependent behavior of PMA. The proposed TS fuzzy system is found to perform better in terms of accuracy and maximum deviation when compared to the previous approaches in the literature.

scholarly journals Assessment of the efficiency of educational project management using neuro-fuzzy system

2019 ◽  
Vol 110 ◽  
pp. 02070
Author(s):  
Mikhail Krichevsky ◽  
Artyr Bydagov ◽  
Julia Martynova
Keyword(s):  
Artificial Intelligence ◽  
Project Management ◽  
Fuzzy System ◽  
Software Package ◽  
Fuzzy Inference ◽  
Type System ◽  
Inference System ◽  
Machine Learning Methods ◽  
System Type ◽  
Neuro Fuzzy

The project represents the introduction of elements and methods of artificial intelligence in the work programs of disciplines in the direction of “Management”. To assess the efficiency of such project management, it was proposed to use tools related to machine learning methods that include neural networks and fuzzy logic. The results of such an assessment are obtained using a neuro-fuzzy anfis (adaptive neuro-fuzzy inference system) type system, which is implemented using the MATLAB R2018b software package.

scholarly journals Investigation on performance of particle swarm optimization (PSO) algorithm based fuzzy inference system (PSOFIS) in a combination of CFD modeling for prediction of fluid flow

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Meisam Babanezhad ◽  
Iman Behroyan ◽  
Ali Taghvaie Nakhjiri ◽  
Azam Marjani ◽  
Mashallah Rezakazemi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Particle Swarm Optimization ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Bubble Column ◽  
Pso Algorithm ◽  
Cfd Modeling ◽  
Swarm Optimization ◽  
Inference System ◽  
Mesh Density

AbstractHerein, a reactor of bubble column type with non-equilibrium thermal condition between air and water is mechanistically modeled and simulated by the CFD technique. Moreover, the combination of the adaptive network (AN) trainer with the fuzzy inference system (FIS) as the artificial intelligence method calling ANFIS has already shown potential in the optimization of CFD approach. Although the artificial intelligence method of particle swarm optimization (PSO) algorithm based fuzzy inference system (PSOFIS) has a good background for optimizing the other fields of research, there are not any investigations on the cooperation of this method with the CFD. The PSOFIS can reduce all the difficulties and simplify the investigation by elimination of the additional CFD simulations. In fact, after achieving the best intelligence, all the predictions can be done by the PSOFIS instead of the massive computational efforts needed for CFD modeling. The first aim of this study is to develop the PSOFIS for use in the CFD approach application. The second one is to make a comparison between the PSOFIS and ANFIS for the accurate prediction of the CFD results. In the present study, the CFD data are learned by the PSOFIS for prediction of the water velocity inside the bubble column. The values of input numbers, swarm sizes, and inertia weights are investigated for the best intelligence. Once the best intelligence is achieved, there is no need to mesh refinement in the CFD domain. The mesh density can be increased, and the newer predictions can be done in an easier way by the PSOFIS with much less computational efforts. For a strong verification, the results of the PSOFIS in the prediction of the liquid velocity are compared with those of the ANFIS. It was shown that for the same fuzzy set parameters, the PSOFIS predictions are closer to the CFD in comparison with the ANFIS. The regression number (R) of the PSOFIS (0.98) was a little more than that of the ANFIS (0.97). The PSOFIS showed a powerful potential in mesh density increment from 9477 to 774,468 and accurate predictions for the new nodes independent of the CFD modeling.

scholarly journals Prediction of Multi-Scalar Standardized Precipitation Index by Using Artificial Intelligence and Regression Models

Climate ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 28
Author(s):  
Anurag Malik ◽  
Anil Kumar ◽  
Priya Rai ◽  
Alban Kuriqi
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Inference ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Vital Role ◽  
Precipitation Index ◽  
Visual Interpretation ◽  
Inference System ◽  
Drought Prediction ◽  
Optimal Functioning

Accurate monitoring and forecasting of drought are crucial. They play a vital role in the optimal functioning of irrigation systems, risk management, drought readiness, and alleviation. In this work, Artificial Intelligence (AI) models, comprising Multi-layer Perceptron Neural Network (MLPNN) and Co-Active Neuro-Fuzzy Inference System (CANFIS), and regression, model including Multiple Linear Regression (MLR), were investigated for multi-scalar Standardized Precipitation Index (SPI) prediction in the Garhwal region of Uttarakhand State, India. The SPI was computed on six different scales, i.e., 1-, 3-, 6-, 9-, 12-, and 24-month, by deploying monthly rainfall information of available years. The significant lags as inputs for the MLPNN, CANFIS, and MLR models were obtained by utilizing Partial Autocorrelation Function (PACF) with a significant level equal to 5% for SPI-1, SPI-3, SPI-6, SPI-9, SPI-12, and SPI-24. The predicted multi-scalar SPI values utilizing the MLPNN, CANFIS, and MLR models were compared with calculated SPI of multi-time scales through different performance evaluation indicators and visual interpretation. The appraisals of results indicated that CANFIS performance was more reliable for drought prediction at Dehradun (3-, 6-, 9-, and 12-month scales), Chamoli and Tehri Garhwal (1-, 3-, 6-, 9-, and 12-month scales), Haridwar and Pauri Garhwal (1-, 3-, 6-, and 9-month scales), Rudraprayag (1-, 3-, and 6-month scales), and Uttarkashi (3-month scale) stations. The MLPNN model was best at Dehradun (1- and 24- month scales), Tehri Garhwal and Chamoli (24-month scale), Haridwar (12- and 24-month scales), Pauri Garhwal (12-month scale), Rudraprayag (9-, 12-, and 24-month), and Uttarkashi (1- and 6-month scales) stations, while the MLR model was found to be optimal at Pauri Garhwal (24-month scale) and Uttarkashi (9-, 12-, and 24-month scales) stations. Furthermore, the modeling approach can foster a straightforward and trustworthy expert intelligent mechanism for projecting multi-scalar SPI and decision making for remedial arrangements to tackle meteorological drought at the stations under study.

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.

scholarly journals Artificial Intelligence Techniques for river flow forecasting in the Seyhan River Catchment, Turkey

2007 ◽  
Vol 4 (3) ◽  
pp. 1369-1406 ◽  
Author(s):  
M. Firat
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
River Flow ◽  
Fuzzy Inference ◽  
Flow Data ◽  
Data Set ◽  
Inference System ◽  
Forecasting Models ◽  
River Flow Forecasting ◽  
Best Fit

Abstract. The use of Artificial Intelligence methods is becoming increasingly common in the modeling and forecasting of hydrological and water resource processes. In this study, applicability of Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) methods, Generalized Regression Neural Networks (GRNN) and Feed Forward Neural Networks (FFNN), for forecasting of daily river flow is investigated and the Seyhan catchment, located in the south of Turkey, is chosen as a case study. Totally, 5114 daily river flow data are obtained from river flow gauges station of Üçtepe (1818) on Seyhan River between the years 1986 and 2000. The data set are divided into three subgroups, training, testing and verification. The training and testing data set include totally 5114 daily river flow data and the number of verification data points is 731. The river flow forecasting models having various input structures are trained and tested to investigate the applicability of ANFIS and ANN methods. The results of ANFIS, GRNN and FFNN models for both training and testing are evaluated and the best fit forecasting model structure and method is determined according to criteria of performance evaluation. The best fit model is also trained and tested by traditional statistical methods and the performances of all models are compared in order to get more effective evaluation. Moreover ANFIS, GRNN and FFNN models are also verified by verification data set including 731 daily river flow data at the time period 1998–2000 and the results of models are compared. The results demonstrate that ANFIS model is superior to the GRNN and FFNN forecasting models, and ANFIS can be successfully applied and provide high accuracy and reliability for daily River flow forecasting.

scholarly journals A fuzzy system for cloacal temperature prediction of broiler chickens

2012 ◽  
Vol 42 (1) ◽  
pp. 166-171 ◽  
Author(s):  
Leandro Ferreira ◽  
Tadayuki Yanagi Junior ◽  
Wilian Soares Lacerda ◽  
Giovanni Francisco Rabelo
Keyword(s):  
Decision Support ◽  
Fuzzy System ◽  
Broiler Chickens ◽  
Broiler Chicken ◽  
Fuzzy Inference ◽  
Air Velocity ◽  
Output Variable ◽  
Average Standard Deviation ◽  
Inference System ◽  
Input Variables

Cloacal temperature (CT) of broiler chickens is an important parameter to classify its comfort status; therefore its prediction can be used as decision support to turn on acclimatization systems. The aim of this research was to develop and validate a system using the fuzzy set theory for CT prediction of broiler chickens. The fuzzy system was developed based on three input variables: air temperature (T), relative humidity (RH) and air velocity (V). The output variable was the CT. The fuzzy inference system was performed via Mamdani's method which consisted in 48 rules. The defuzzification was done using center of gravity method. The fuzzy system was developed using MAPLE® 8. Experimental results, used for validation, showed that the average standard deviation between simulated and measured values of CT was 0.13°C. The proposed fuzzy system was found to satisfactorily predict CT based on climatic variables. Thus, it could be used as a decision support system on broiler chicken growth.

Biometric Identification System Using Neuro and Fuzzy Computational Approaches

2016 ◽  
pp. 335-366
Author(s):  
Tripti Rani Borah ◽  
Kandarpa Kumar Sarma ◽  
Pranhari Talukdar
Keyword(s):  
Fuzzy System ◽  
Fuzzy Inference ◽  
Biometric Identification ◽  
Identification System ◽  
Inference System ◽  
Computational Approaches ◽  
Neuro Fuzzy ◽  
Actual Design ◽  
Authentication System ◽  
Numeric Data

In all authentication systems, biometric samples are regarded to be the most reliable one. Biometric samples like fingerprint, retina etc. is unique. Most commonly available biometric system prefers these samples as reliable inputs. In a biometric authentication system, the design of decision support system is critical and it determines success or failure. Here, we propose such a system based on neuro and fuzzy system. Neuro systems formulated using Artificial Neural Network learn from numeric data while fuzzy based approaches can track finite variations in the environment. Thus NFS systems formed using ANN and fuzzy system demonstrate adaptive, numeric and qualitative processing based learning. These attributes have motivated the formulation of an adaptive neuro fuzzy inference system which is used as a DSS of a biometric authenticable system. The experimental results show that the system is reliable and can be considered to be a part of an actual design.

An Artificial Intelligence Based Approach for High Impedance Faults Analysis in Distribution Networks

2012 ◽  
Vol 1 (2) ◽  
pp. 44-59 ◽  
Author(s):  
M. S. Abdel Aziz ◽  
M. A. Moustafa Hassan ◽  
E. A. El-Zahab
Keyword(s):  
Artificial Intelligence ◽  
Distribution System ◽  
Fault Location ◽  
Fuzzy Inference ◽  
Distribution Networks ◽  
Inference System ◽  
New Approach ◽  
High Impedance ◽  
Neuro Fuzzy ◽  
High Impedance Faults

This paper presents a new approach for high impedance faults analysis (detection, classification and location) in distribution networks using Adaptive Neuro Fuzzy Inference System. The proposed scheme was trained by data from simulation of a distribution system under various faults conditions and tested for different system conditions. Details of the design process and the results of performance using the proposed method are discussed. The results show the proposed technique effectiveness in detecting, classifying, and locating high impedance faults. The 3rd harmonics, magnitude and angle, for the 3 phase currents give superior results for fault detection as well as for fault location in High Impedance faults. The fundamental components magnitude and angle for the 3 phase currents give superior results for classification phase of High Impedance faults over other types of data inputs.

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