A Review of Training Methods of ANFIS for Applications in Business and Economics

Salleh, Mohd Najib Mohd; Hussain, Kashif

doi:10.14257/ijunesst.2016.9.7.17

Cited by 33 publications

(19 citation statements)

References 28 publications

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“…Few research papers have approached this question in line with our methodology. Salleh and Hussain (2016) refer to applications of ANFIS (excluding GA), noting that such methdods require training parameters effectively so as to perform efficiently as the inputs count increases. In terms of applications, they write of financial applications, namely, to predict financial crises, bankruptcy, credit risk, currency, stocks prices, and gold prices.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In terms of applications, they write of financial applications, namely, to predict financial crises, bankruptcy, credit risk, currency, stocks prices, and gold prices. (Salleh et al, 2016) Similar ANFIS-hybrid methods in the literature of time-series forecasting include autoregressive adaptive network fuzzy inference system (AR-ANFIS) by Sarıca et al (2016), a hybrid model of ARIMA (Auto Regressive Integrated Moving Average) and ANFIS by Barak and Sadegh (2016), and a Quantum-behaved PSO (Particle Swarm Optimization) and ANFIS by Bagheri et al (2016).…”

Section: Literature Reviewmentioning

confidence: 99%

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Performance Evaluation of ANFIS and GA-ANFIS for Predicting Stock Market Indices

Ghashami¹,

Kamyar²

2021

IJEF

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A model of Adaptive Neuro-Fuzzy Inference System (ANFIS) trained with an evolutionary algorithm, namely Genetic Algorithm (GA) is presented in this paper. Further, the model is tested on the NASDAQ stock market indices which is among the most widely followed indices in the United States. Empirical results show that by determining the parameters of ANFIS (premise and consequent parameters) using GA, we can improve performance in terms of Mean Squared Error (MSE), Root Mean Squared Error (RMSE), coefficient of determination (R-Squared) in comparison with using solely ANFIS.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Performance Evaluation of ANFIS and GA-ANFIS for Predicting Stock Market Indices

Ghashami¹,

Kamyar²

2021

IJEF

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“…It is possible to say that the PSO algorithm is generally used in the publications related to the training of ANFIS. Only parameter learning was performed in the publications in which ANFIS based estimation models were developed for the estimation of electricity costs, wind energy and customer happiness for a new product [15][16][17][18][19]. Turki (2012) [20] used the ANFIS trained by PSO for nonlinear system adaptive control.…”

Section: Related Workmentioning

confidence: 99%

Training Anfis System with Moth-Flame Optimization Algorithm

Canayaz

2019

ijisae

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Adaptive Neuro Fuzzy Inference System (ANFIS) is an adaptive network that can use the computation and learning abilities of artificial neural network together with the inference feature of fuzzy logic. The ANFIS system, which is used in the solution of many problems such as classification and estimation of deep learning applications, meets the needs in many different areas such as modeling, control, and parameter estimation. In recent years, heuristic methods have been used for the training of this network, which requires initial and result parameters by its structure. Moth-Flame Optimization Algorithm (MFO) is one of the current heuristic methods modeled by the influence of the spiral movement of the moths towards the light source. In this study, the MFO algorithm was used for the first time for the optimization of initial and result parameters in the ANFIS system. In the determination of parameters, nonlinear system identification, time series estimation, classification problems were tried to be solved. When the results obtained for the ANFIS trained with the known heuristic methods such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Whale Optimization Algorithm (WOA) and the results of ANFIS trained by the MFO were examined, it was observed that the MFO had lower error values.

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“…It is a framework of neuro-fuzzy model that can integrate human expertise as well as adapt itself through learning. As an adaptive neuro-fuzzy model; it has advantage of being flexible, adaptive and effective for non-linear complex problems [6]. Recently, ANFIS has been successfully applied to the applications involving classification, rule-based process controls and pattern recognition.…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

“…The original ANFIS architecture that was introduced by Jang has drawbacks, since it uses hybrid learning algorithm which is the combination of GD and LSE. Because of using GD, it has problem to be likely trapped in local [6]. To cope with this many researchers have proposed meta-heuristic algorithms; such as, genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC) etc.…”

Section: Comparative Study Of Anfismentioning

confidence: 99%

A Modified Neuro-Fuzzy System Using Metaheuristic Approaches for Data Classification

Salleh¹,

Talpur²,

HussainTalpur³

2018

Artificial Intelligence - Emerging Trends and Applications

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The impact of innovated Neuro-Fuzzy System (NFS) has emerged as a dominant technique for addressing various difficult research problems in business. ANFIS (Adaptive Neuro-Fuzzy Inference system) is an efficient combination of ANN and fuzzy logic for modeling highly non-linear, complex and dynamic systems. It has been proved that, with proper number of rules, an ANFIS system is able to approximate every plant. Even though it has been widely used, ANFIS has a major drawback of computational complexities. The number of rules and its tunable parameters increase exponentially when the numbers of inputs are large. Moreover, the standard learning process of ANFIS involves gradient based learning which has prone to fall in local minima. Many researchers have used meta-heuristic algorithms to tune parameters of ANFIS. This study will modify ANFIS architecture to reduce its complexity and improve the accuracy of classification problems. The experiments are carried out by trying different types and shapes of membership functions and meta-heuristics Artificial Bee Colony (ABC) algorithm with ANFIS and the training error results are measured for each combination. The results showed that modified ANFIS combined with ABC method provides better training error results than common ANFIS model.

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A Review of Training Methods of ANFIS for Applications in Business and Economics

Cited by 33 publications

References 28 publications

Performance Evaluation of ANFIS and GA-ANFIS for Predicting Stock Market Indices

Performance Evaluation of ANFIS and GA-ANFIS for Predicting Stock Market Indices

Training Anfis System with Moth-Flame Optimization Algorithm

A Modified Neuro-Fuzzy System Using Metaheuristic Approaches for Data Classification

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