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Journal ArticleDOI

ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey

18 Aug 2015-International Journal of Computer Applications (Foundation of Computer Science (FCS), NY, USA)-Vol. 123, Iss: 13, pp 32-38
TL;DR: The architecture and basic learning process underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inferenceSystem implemented in the framework of adaptive networks.
Abstract: paper, we presented the architecture and basic learning process underlying ANFIS (adaptive-network-based fuzzy inference system) which is a fuzzy inference system implemented in the framework of adaptive networks. Soft computing approaches including artificial neural networks and fuzzy inference have been used widely to model expert behavior. Using given input/output data values, the proposed ANFIS can construct mapping based on both human knowledge (in the form of fuzzy if-then rules) and hybrid learning algorithm. In modeling and simulation, the ANFIS strategy is employed to model nonlinear functions, to control one of the most important parameters of the induction machine and predict a chaotic time series, all yielding more effective, faster response or settling times.

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Citations
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Journal ArticleDOI
TL;DR: The heuristic and hybrid approaches utilized in ANFIS training are examined in order to guide researchers in their study and it has been observed that there is a trend toward heuristic based ANfIS training algorithms for better performance recently.
Abstract: In the structure of ANFIS, there are two different parameter groups: premise and consequence. Training ANFIS means determination of these parameters using an optimization algorithm. In the first ANFIS model developed by Jang, a hybrid learning approach was proposed for training. In this approach, while premise parameters are determined by using gradient descent (GD), consequence parameters are found out with least squares estimation (LSE) method. Since ANFIS has been developed, it is used in modelling and identification of numerous systems and successful results have been achieved. The selection of optimization method utilized in training is very important to get effective results with ANFIS. It is seen that derivate based (GD, LSE etc.) and non-derivative based (heuristic algorithms such us GA, PSO, ABC etc.) algorithms are used in ANFIS training. Nevertheless, it has been observed that there is a trend toward heuristic based ANFIS training algorithms for better performance recently. At the same time, it seems to be proposed in derivative and heuristic based hybrid algorithms. Within the scope of this study, the heuristic and hybrid approaches utilized in ANFIS training are examined in order to guide researchers in their study. In addition, the final status in ANFIS training is evaluated and it is aimed to shed light on further studies related to ANFIS training.

454 citations

Journal ArticleDOI
TL;DR: The developed ANN model has been introduced as the best predictive technique for solving problem of the compressive strength of mortars and an ambitious attempt to reveal the nature of mortar materials has been made.
Abstract: Despite the extensive use of mortars materials in constructions over the last decades, there is not yet a reliable and robust method, available in the literature, which can estimate its strength based on its mix parameters. This limitation is due to the highly nonlinear relation between the mortar’s compressive strength and the mixed components. In this paper, the application of artificial intelligence techniques toward the prediction of the compressive strength of cement-based mortar materials with or without metakaolin has been investigated. Specifically, surrogate models (such as artificial neural network, ANN and adaptive neuro-fuzzy inference system, ANFIS models) have been developed to the prediction of the compressive strength of mortars trained using experimental data available in the literature. The comparison of the derived results with the experimental findings demonstrates the ability of both ANN and ANFIS models to approximate the compressive strength of mortars in a reliable and robust manner. Although ANFIS was able to obtain higher performance prediction to estimate the compressive strength of mortars compared to ANN model, it was found through the verification process of some other additional data, the ANFIS model has overfitted the data. Therefore, the developed ANN model has been introduced as the best predictive technique for solving problem of the compressive strength of mortars. Furthermore, using the optimum developed model an ambitious attempt to reveal the nature of mortar materials has been made.

187 citations

Journal ArticleDOI
TL;DR: This paper presents an Adaptive Neuro-Fuzzy Inference System (ANFIS) based robust scheme using the integrated features of the text, images and frames for web-phishing detection and protection and achieves 98.3% accuracies.
Abstract: A phishing attack is one of the most significant problems faced by online users because of its enormous effect on the online activities performed. In recent years, phishing attacks continue to escalate in frequency, severity and impact. Several solutions, using various methodologies, have been proposed in the literature to counter the web-phishing threats. Notwithstanding, the existing technology cannot detect the new phishing attacks accurately due to the insufficient integration of features of the text, image and frame in the evaluation process. The use of related features of images, frames and text of legitimate and non-legitimate websites and associated artificial intelligence algorithms to develop an integrated method to address these together. This paper presents an Adaptive Neuro-Fuzzy Inference System (ANFIS) based robust scheme using the integrated features of the text, images and frames for web-phishing detection and protection. The proposed solution achieves 98.3% accuracies. To our best knowledge, this is the first work that considers the best-integrated text, image and frame feature based solution for phishing detection scheme.

90 citations

Journal ArticleDOI
23 Jun 2020
TL;DR: In the past decades, due to inflation of urbanized area and climate change, a large number of landslides have been reported as mentioned in this paper, which is caused by specific compositional slope movement.
Abstract: Landslide, one of the most critical natural hazards, is caused due to specific compositional slope movement In the past decades, due to inflation of urbanized area and climate change, a c

90 citations

Journal ArticleDOI
01 Sep 2019
TL;DR: This paper employs Neuro-Fuzzy Model to secure data at the smart gateway, then the IoT device selects an optimal Fog node to which it can offload its workload using Particle Swarm Optimization via the smart Gateway, and proposes a secure computation offloading scheme in Fog-Cloud-IoT environment (SecOFF-FCIiT).
Abstract: Computation offloading is one of the important application in Internet of Things (IoT) ecosystem. Computational offloading provides assisted means of processing large amounts of data generated by abundant IoT devices, speed up processing of intensive tasks and save battery life. In this paper, we propose a secure computation offloading scheme in Fog-Cloud-IoT environment (SecOFF-FCIoT). Using machine learning strategies, we accomplish efficient, secure offloading in Fog-IoT setting. In particular, we employ Neuro-Fuzzy Model to secure data at the smart gateway, then the IoT device selects an optimal Fog node to which it can offload its workload using Particle Swarm Optimization(PSO) via the smart gateway. If the fog node is not capable of handling the workload, it is forwarded to the cloud after being classified as either sensitive or non-sensitive. Sensitive data is maintained in private cloud. Whereas non-sensitive data is offloaded using dynamic offloading strategy. In PSO, the availability of fog node is computed using two metrics; i) Available Processing Capacity (APC), and ii) Remaining Node Energy (RNE). Selection of cloud is based on Reinforcement Learning. Our proposed approach is implemented for smart city applications using NS-3 simulator with JAVA Programming. We compare our proposed secure computation offloading model with previous approaches which include DTO-SO, FCFS, LOTEC, and CMS-ACO. Simulation results show that our proposed scheme minimizes latency as compared to selected benchmarks.

76 citations

References
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Journal ArticleDOI
01 May 1993
TL;DR: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference System implemented in the framework of adaptive networks.
Abstract: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation, the ANFIS architecture is employed to model nonlinear functions, identify nonlinear components on-line in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificial neural networks and earlier work on fuzzy modeling are listed and discussed. Other extensions of the proposed ANFIS and promising applications to automatic control and signal processing are also suggested. >

15,085 citations


"ANFIS: Adaptive Neuro-Fuzzy Inferen..." refers background or methods or result in this paper

  • ...Further, author compared system with artificial neural networks and preliminary tested [1]....

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  • ...In particular, the control technique based on fuzzy modeling or fuzzy identification was first systematically introduced by Takagi and Sugeno [1], has found numerous applications in fuzzy control, for medical diagnosis [3], decision-making and solve problems based on data mining [4]....

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  • ...A comprehensive study of existing work in assorted areas using soft computing methodologies specifically focusing on neural networks and fuzzy logic can be found in [1]....

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  • ...A computational technique to deal with non-linear and complex problem was discussed by J.R Jang (1993)....

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Journal ArticleDOI
TL;DR: This article proposes to bring the various neuro-fuzzy models used for rule generation under a unified soft computing framework, and includes both rule extraction and rule refinement in the broader perspective of rule generation.
Abstract: The present article is a novel attempt in providing an exhaustive survey of neuro-fuzzy rule generation algorithms. Rule generation from artificial neural networks is gaining in popularity in recent times due to its capability of providing some insight to the user about the symbolic knowledge embedded within the network. Fuzzy sets are an aid in providing this information in a more human comprehensible or natural form, and can handle uncertainties at various levels. The neuro-fuzzy approach, symbiotically combining the merits of connectionist and fuzzy approaches, constitutes a key component of soft computing at this stage. To date, there has been no detailed and integrated categorization of the various neuro-fuzzy models used for rule generation. We propose to bring these together under a unified soft computing framework. Moreover, we include both rule extraction and rule refinement in the broader perspective of rule generation. Rules learned and generated for fuzzy reasoning and fuzzy control are also considered from this wider viewpoint. Models are grouped on the basis of their level of neuro-fuzzy synthesis. Use of other soft computing tools like genetic algorithms and rough sets are emphasized. Rule generation from fuzzy knowledge-based networks, which initially encode some crude domain knowledge, are found to result in more refined rules. Finally, real-life application to medical diagnosis is provided.

726 citations


"ANFIS: Adaptive Neuro-Fuzzy Inferen..." refers background or methods in this paper

  • ...A comprehensive survey of neuro–fuzzy rule generation algorithms for real-time applications is examined by S. Mitra et al. (2000)....

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  • ...Based on fuzzy inference system, real-life application to medical diagnosis is provided [15]....

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  • ...Fuzzy sets are an aid in providing symbolic knowledge information in a more human understandable or natural form, and can hold uncertainties at various levels [15]....

    [...]

  • ...This approach approximates a nonlinear system with a combination of several linear systems, by decomposing the whole input space into several partial fuzzy spaces and the output of each rule is a linear arrangement of input variables plus a constant term [15]....

    [...]

  • ...This type of knowledge representation does not allow the output variables to be described in linguistic terms and the parameter optimization is carried out iteratively using a nonlinear optimization method [15]....

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Journal ArticleDOI
TL;DR: The paper analyzes the main methods for automatic rule generation and structure optimization and grouped them into several families and compared according to the rule interpretability criterion.
Abstract: Fuzzy inference systems (FIS) are widely used for process simulation or control. They can be designed either from expert knowledge or from data. For complex systems, FIS based on expert knowledge only may suffer from a loss of accuracy. This is the main incentive for using fuzzy rules inferred from data. Designing a FIS from data can be decomposed into two main phases: automatic rule generation and system optimization. Rule generation leads to a basic system with a given space partitioning and the corresponding set of rules. System optimization can be done at various levels. Variable selection can be an overall selection or it can be managed rule by rule. Rule base optimization aims to select the most useful rules and to optimize rule conclusions. Space partitioning can be improved by adding or removing fuzzy sets and by tuning membership function parameters. Structure optimization is of a major importance: selecting variables, reducing the rule base and optimizing the number of fuzzy sets. Over the years, many methods have become available for designing FIS from data. Their efficiency is usually characterized by a numerical performance index. However, for human-computer cooperation another criterion is needed: the rule interpretability. An implicit assumption states that fuzzy rules are by nature easy to be interpreted. This could be wrong when dealing with complex multivariable systems or when the generated partitioning is meaningless for experts. The paper analyzes the main methods for automatic rule generation and structure optimization. They are grouped into several families and compared according to the rule interpretability criterion. For this purpose, three conditions for a set of rules to be interpretable are defined.

709 citations

Journal ArticleDOI
TL;DR: In this paper, an artificial-intelligence-based solution to interface and deliver maximum power from a photovoltaic (PV) power generating system in standalone operation is proposed, where the interface between the PV dc source and the load is accomplished by a quasi-Z-source inverter.
Abstract: The paper proposes an artificial-intelligence-based solution to interface and deliver maximum power from a photovoltaic (PV) power generating system in standalone operation. The interface between the PV dc source and the load is accomplished by a quasi-Z-source inverter (qZSI). The maximum power delivery to the load is ensured by an adaptive neuro-fuzzy inference system (ANFIS) based on maximum power point tracking (MPPT). The proposed ANFIS-based MPPT offers an extremely fast dynamic response with high accuracy. The closed-loop control of the qZSI regulates the shoot through duty ratio and the modulation index to effectively control the injected power and maintain the stringent voltage, current, and frequency conditions. The proposed technique is tested for isolated load conditions. Simulation and experimental approaches are used to validate the proposed scheme.

226 citations


"ANFIS: Adaptive Neuro-Fuzzy Inferen..." refers methods in this paper

  • ...Simulation and experimental approaches are used to validate the proposed scheme [22]....

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  • ...Abu-Rub et al. (2013) presented an application of ANFIS for maximum power delivery to the load based on maximum power point tracking....

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Journal ArticleDOI
01 Jun 2009
TL;DR: This study detects which methodology or methodologies of SC are used frequently together to solve the special problems of medicine and introduces briefly the various SC methodologies and presents various applications in medicine between the years 2000 and 2008.
Abstract: Soft computing (SC) is not a new term; we have gotten used to reading and hearing about it daily. Nowadays, the term is used often in computer science and information technology. It is possible to define SC in different ways. Nonetheless, SC is a consortium of methodologies which works synergistically and provides, in one form or another, flexible information processing capability for handling real life ambiguous situations. Its aim is to exploit the tolerance for imprecision, uncertainty, approximate reasoning and partial truth in order to achieve tractability, robustness and low-cost solutions. SC includes fuzzy logic (FL), neural networks (NNs), and genetic algorithm (GA) methodologies. SC combines these methodologies as FL and NN (FL-NN), NN and GA (NN-GA) and FL and GA (FL-GA). Recent years have witnessed the phenomenal growth of bio-informatics and medical informatics by using computational techniques for interpretation and analysis of biological and medical data. Among the large number of computational techniques used, SC, which incorporates neural networks, evolutionary computation, and fuzzy systems, provides unmatched utility because of its demonstrated strength in handling imprecise information and providing novel solutions to hard problems. The aim of this paper is to introduce briefly the various SC methodologies and to present various applications in medicine between the years 2000 and 2008. The scope is to demonstrate the possibilities of applying SC to medicine-related problems. The recent published knowledge about use of SC in medicine is researched in MEDLINE. This study detects which methodology or methodologies of SC are used frequently together to solve the special problems of medicine. According to MEDLINE database searches, the rates of preference of SC methodologies in medicine were found as 68% of FL-NN, 27% of NN-GA and 5% of FL-GA. So far, FL-NN methodology was significantly used in medicine. The rates of using FL-NN in clinical science, diagnostic science and basic science were found as %83, %71 and %48, respectively. On the other hand NN-GA and FL-GA methodologies were mostly preferred by basic science of medicine. Another message emerging from this survey is that the number of papers which used NN-GA methodology has continuously risen until today. Also search results put the case clearly that FL-GA methodology has not applied well enough to medicine yet. Undeniable interest in studying SC methodologies in genetics, physiology, radiology, cardiology, and neurology disciplines proves that studying SC is very fruitful in these disciplines and it is expected that future researches in medicine will use SC more than it is used today to solve more complex problems.

109 citations