Collaborating Authors

Hybrid Adaptive Neuro-Fuzzy Inference System for Diagnosing the Liver Disorders Artificial Intelligence

In this study, a hybrid method based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO) for diagnosing Liver disorders (ANFIS-PSO) is introduced. This smart diagnosis method deals with a combination of making an inference system and optimization process which tries to tune the hyper-parameters of ANFIS based on the data-set. The Liver diseases characteristics are taken from the UCI Repository of Machine Learning Databases. The number of these characteristic attributes are 7, and the sample number is 354. The right diagnosis performance of the ANFIS-PSO intelligent medical system for liver disease is evaluated by using classification accuracy, sensitivity and specificity analysis, respectively. According to the experimental results, the performance of ANFIS-PSO can be more considerable than traditional FIS and ANFIS without optimization phase.

Modeling and Uncertainty Analysis of Groundwater Level Using Six Evolutionary Optimization Algorithms Hybridized with ANFIS, SVM, and ANN Machine Learning

In the present study, six meta-heuristic schemes are hybridized with artificial neural network (ANN), adaptive neuro-fuzzy interface system (ANFIS), and support vector machine (SVM), to predict monthly groundwater level (GWL), evaluate uncertainty analysis of predictions and spatial variation analysis. The six schemes, including grasshopper optimization algorithm (GOA), cat swarm optimization (CSO), weed algorithm (WA), genetic algorithm (GA), krill algorithm (KA), and particle swarm optimization (PSO), were used to hybridize for improving the performance of ANN, SVM, and ANFIS models. Groundwater level (GWL) data of Ardebil plain (Iran) for a period of 144 months were selected to evaluate the hybrid models. The pre-processing technique of principal component analysis (PCA) was applied to reduce input combinations from monthly time series up to 12-month prediction intervals. The results showed that the ANFIS-GOA was superior to the other hybrid models for predicting GWL in the first piezometer and third piezometer in the testing stage. The performance of hybrid models with optimization algorithms was far better than that of classical ANN, ANFIS, and SVM models without hybridization. The percent of improvements in the ANFIS-GOA versus standalone ANFIS in piezometer 10 were 14.4%, 3%, 17.8%, and 181% for RMSE, MAE, NSE, and PBIAS in the training stage and 40.7%, 55%, 25%, and 132% in testing stage, respectively. The improvements for piezometer 6 in train step were 15%, 4%, 13%, and 208% and in the test step were 33%, 44.6%, 16.3%, and 173%, respectively, that clearly confirm the superiority of developed hybridization schemes in GWL modeling. Uncertainty analysis showed that ANFIS-GOA and SVM had, respectively, the best and worst performances among other models. In general, GOA enhanced the accuracy of the ANFIS, ANN, and SVM models.

Deep Neural Networks and Neuro-Fuzzy Networks for Intellectual Analysis of Economic Systems Artificial Intelligence

In tis paper we consider approaches for time series forecasting based on deep neural networks and neuro-fuzzy nets. Also, we make short review of researches in forecasting based on various models of ANFIS models. Deep Learning has proven to be an effective method for making highly accurate predictions from complex data sources. Also, we propose our models of DL and Neuro-Fuzzy Networks for this task. Finally, we show possibility of using these models for data science tasks. This paper presents also an overview of approaches for incorporating rule-based methodology into deep learning neural networks.

Integrated attitude estimation and control of satellite with thruster actuator using ANFIS Machine Learning

This paper proposed a new estimation and control strategy to control the satellite attitude. As the attitude control strategy plays an essential role in the different kinds of space missions, scientists try to improve the performance of the satellite attitude system, regardless of the expense. In this study, we proposed an adaptive neuro-fuzzy integrated (ANFIS) satellite attitude estimation and control system. A pulse modulator is used to generate the right ON/OFF commands of the thruster actuator. To evaluate the performance of the ANFIS controller in closed-loop simulation, an ANFIS observer is used to estimate the attitude and angular velocities of the satellite using a magnetometer, sun sensor, and rate gyro data. Besides, a new ANFIS system will be proposed and evaluated that can simultaneously control and estimate the system. The performance of the ANFIS controller is compared with the optimal PID controller in a Monte Carlo simulation using different initial conditions, disturbance, and noise. The simulations are performed to verify the ANFIS controller's ability to decrease settling time and fuel consumption in comparison with the optimal PID controller. Also, examine the ANFIS estimator, and the results demonstrate the high skill of these designated observers. Moreover, we proposed an integrated ANFIS estimator and controller for satellite attitude control and estimation in the presence of noise and uncertainty, which can reduce the computational effort and offer smooth actuator actions.

Forecasting Solar Activity with Two Computational Intelligence Models (A Comparative Study) Artificial Intelligence

Solar activity It is vital to accurately predict solar activity, in order to decrease the plausible damage of electronic equipment in the event of a large high-intensity solar eruption. Recently, we have proposed BELFIS (Brain Emotional Learning-based Fuzzy Inference System) as a tool for the forecasting of chaotic systems. The structure of BELFIS is designed based on the neural structure of fear conditioning. The function of BELFIS is implemented by assigning adaptive networks to the components of the BELFIS structure. This paper especially focuses on performance evaluation of BELFIS as a predictor by forecasting solar cycles 16 to 24. The performance of BELFIS is compared with other computational models used for this purpose, and in particular with adaptive neuro-fuzzy inference system (ANFIS).