Genetically optimized ANFIS based Intelligent Navigation System

Malleswaran, M.; Vaidehi, V.; Joseph, Rabin

doi:10.1109/icoac.2011.6165207

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Soleimani and Salmalian [20] deployed GA and Singular Value Decomposition (SVD) for the optimum design of both Gaussian MFs and linear coefficients of the network, respectively. GA in combination with least square methods is implemented by Soto et al [21] and Malleswaran et al [22] to acquire optimum ANFIS network. The prior research used ANFIS for solving classification problem, while the later one chose ANFIS to predict the values of Longitude and Altitude.…”

Section: Training Methods Of Anfismentioning

confidence: 99%

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

Salleh¹,

Hussain²

2016

IJUNESST

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Fuzzy Neural Networks (FNNs) techniques have been effectively used in applications that range from medical to mechanical engineering, to business and economics. Despite of attracting researchers in recent years and outperforming other fuzzy systems, Adaptive Neuro-Fuzzy Inference System (ANFIS) still needs effective parameter training and rulebase optimization methods to perform efficiently when the number of inputs increase. Moreover, the standard gradient based learning via two pass learning algorithm is prone slow and prone to get stuck in local minima. Therefore many researchers have trained ANFIS parameters using metaheuristic algorithms however very few have considered optimizing the ANFIS rule-base. Mostly Particle Swarm Optimization (PSO) and its variants have been applied for training approaches used. Other than that, Genetic Algorithm (GA), Firefly Algorithm (FA), Ant Bee Colony (ABC) optimization methods have been employed for effective training of ANFIS networks when solving various problems in the field of business and finance.

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Section: Training Methods Of Anfismentioning

confidence: 99%

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

Salleh¹,

Hussain²

2016

IJUNESST

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Neuro-Fuzzy Methods of Forecasting Quantitative Indicators of Logistic Activity

Lamekhov,

Korovyakovsky

2024

Lecture Notes in Networks and Systems

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Design Algorithm for Forecasting Model of Transport-Logistic Activity on the Basis of Neuro-Fuzzy Networks

Lamehov

Korovyakovskiy

2022

Bulletin of Scientific Research Results

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Purpose: To consider the current situation in container transportation market of Leningrad Region. To show the need in the design of new forecasting models. To justify the use of neural fuzzy networks. To consider existing limitations of their application. To suggest the ways to build predictive neural fuzzy models for evaluating promising quantitative indicators of logistics activities. Methods: ANFIS, R-ANFIS, Fuzzy-Partitions, SCRG, GD, LSE, PSO, ABC, FA. Results: The necessity of adaptation of neural fuzzy networks for making forecasts in the field of logistics is pointed on, the structure of a promising model is proposed which takes into account the use and analysis of variety of methods on structural and parametric identification. The shortcomings of particular methods of structural and parametric identification, which affect the accuracy of being obtained forecasts, are indicated. Practical importance: The importance of design of accurate predictive models for key quantitative indicators of logistics activities is shown. The factors influencing the implementation of transport and logistics activities are given which of, reliable forecast is impossible in the situation of limited time and resources. For consideration, the article proposes the existing methods of parametric and structural identification of fuzzy neural networks. An algorithm for adapting existing methods to the use in a transport and logistics process is proposed.

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Genetically optimized ANFIS based Intelligent Navigation System

Cited by 3 publications

References 9 publications

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

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

Neuro-Fuzzy Methods of Forecasting Quantitative Indicators of Logistic Activity

Design Algorithm for Forecasting Model of Transport-Logistic Activity on the Basis of Neuro-Fuzzy Networks

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