Adaptive Neuro-Fuzzy Systems

Taher, Ahmad

doi:10.5772/7220

Cited by 103 publications

(46 citation statements)

References 48 publications

(43 reference statements)

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“…However, the inclusion of knowledge experts alone would only lead to manual tuning in the design stage, which involves modifying membership functions and/or rule base of the fuzzy systems. This is due to the lack of knowledge regarding fuzzy systems among knowledge experts, resulting in the wrong location of fuzzy sets and number of rules [24]. Therefore, this will result in too much time consumption and error-prone manual tuning on behalf of the knowledge experts.…”

Section: Adaptive Neuro Fuzzy Inference System (Anfis)mentioning

confidence: 99%

A Review on Classification of the Urban Poverty Using the Artificial Intelligence Method

Zakaria

Hassan

Othman

et al. 2017

Journal of Asian Scientific Research

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Article History Keywords Urban poverty Artificial intelligencePoverty and how it has been assessed and measured is a frequently discussed topic by policy makers and social developers. The identification process in poverty measurement is indeed essential towards acknowledging the poor in the population; hence this needs to be clarified. Malaysia measures poverty by means of poverty line, indicating the unidimensional and inflexible distribution of poor and non-poor especially in urban areas. Many researchers have used fuzzy logic to solve the problem of rigid poor/nonpoor dichotomy. This current trend has been able to augment the gap between the rigid and inflexible classification of poor and non-poor. However, there are still several shortcomings that need attention. For instance, the classification of the poor in fuzzy logic that is based on the average income of households still does not cover on the different range of disadvantage on non-monetary items. Based on these trends, ANFIS is proposed to resolve on the highlighted issues. The winning features of ANFIS, which include on simplicity in implementation, understandable explanation facilities through fuzzy rules, and ease of incorporation of both linguistic and numeric knowledge for problem solving may help in producing better result in classification of the urban poor. Essentially, the neural network is proposed to complement the fuzzy system, hence overcoming the limitations of both fuzzy systems and neural networks. As such, ANFIS method is used in this study to better classify on the poor and non-poor compared to fuzzy rule-based system which is lacking in prediction error rate due to too many variables used. However, this method deteriorates from misclassified poverty indicators; hence this study proposed on ensemble ANFIS to produce more accurate and robust classification results. An ensemble model is usually employed to address the problems of over-fitting, high dimensionality or missing features in the training data. Generally, combining multiple classification models increases predictive performance compared to the use of an individual model alone. Therefore, based on these current trends, this study is aimed to do a review on classification of the urban poverty using the artificial intelligence method.Contribution/ Originality: This study contributes in the existing literature of state-of-the-art urban poverty classification methods from the perspective of the modern Artificial Intelligence approach, which is compared with the conservative econometric model. In addition, the implementation of ANFIS and classification ensemble learning in urban poverty classification are also briefly discussed.

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Section: Adaptive Neuro Fuzzy Inference System (Anfis)mentioning

confidence: 99%

A Review on Classification of the Urban Poverty Using the Artificial Intelligence Method

Zakaria

Hassan

Othman

et al. 2017

Journal of Asian Scientific Research

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“…ANFIS is dynamic and capable of learning from experience with sample data to estimate a function without any mathematical model. The online optimization of the parameters of ANFIS enhances its fast and accurate learning capabilities to achieve the desired output [26,27].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Conjugate Gradient Algorithms Applied to the Neuro-Fuzzy Feedback Linearization-Based Adaptive Control Paradigm for Multiple HVDC Links in AC/DC Power System

Ahmad

Khan

2017

Energies

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Abstract:The existing literature predominantly concentrates on the utilization of the gradient descent algorithm for control systems' design in power systems for stability enhancement. In this paper, various flavors of the Conjugate Gradient (CG) algorithm have been employed to design the online neuro-fuzzy linearization-based adaptive control strategy for Line Commutated Converters' (LCC) High Voltage Direct Current (HVDC) links embedded in a multi-machine test power system. The conjugate gradient algorithms are evaluated based on the damping of electro-mechanical oscillatory modes using MATLAB/Simulink. The results validate that all of the conjugate gradient algorithms have outperformed the gradient descent optimization scheme and other conventional and non-conventional control schemes.

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“…The behavior of a fuzzy neural system can be represented by a set of humanly understandable rules or by a combination of localized basis functions associated with local models, making them an ideal framework to perform nonlinear predictive modeling [33]. One well-known structure is the ANFIS that enables the nonlinear modeling, simulation, and forecasting.…”

Section: Decisionmakermentioning

confidence: 99%

Estimating New Product Success with the Use of Intelligent Systems

Relich

Bzdyra

2014

Foundations of Management

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The paper presents identifying success factors in new product development and selecting new product portfolio. The critical success factors are identified on the basis of an enterprise system, including the fields of project management, marketing and customer's comments concerning the previous products. The model of measuring the success of a product includes the indicators such as duration and cost of product development, and net profit from a product. The proposed methodology is based on identification of the relationships between product success and project environment parameters with the use of artificial neural networks and fuzzy neural system that is compared with the results from linear model. The presented method contains the stages of knowledge discovery process such as data selection, data preprocessing, and data mining in the context of an enterprise resource planning system database. The illustrative example enhances a performance comparison of intelligent systems in the context of data preprocessing.Keywords: project management, new product development, knowledge acquisition, data mining, ERP system. IntroductionNew product development (NPD) is one of the most important processes in maintaining a company's competitive position and continuing business success. New products and innovations impact on sales volume, employment, technological process, and economic progress. Contribution of NPD to the growth of the companies, its influence on profit performance, and its role as a key factor in business planning has been widely considered [e.g. 1-3]. Nevertheless, it is still reported that the success rate of product development projects is unsatisfactory, with more cost and time than expected having been consumed to achieve the project goals.The main reasons why most companies have failed in the development of new products derive from extrinsic and intrinsic problems. Extrinsic problems include flops in the market, changes in regulations or simply competition develops product first [4]. Intrinsic problems concern the limited resource constraints (e.g. temporal, financial, and human) and result in the difficulties to meet the project goals, including product innovativeness. Unsatisfactory success rate of product development projects can also be considered from the perspective of inherent feature of NPD, that is, it is a relatively risky activity [5], as market competition and product technology advancement are often intense [6].Although the success of a new product depends on the environmental uncertainties that are beyond a firm's control, companies should take into account both external and internal indices that can impact on the product success. Internal indices can be acquired from company's databases, including Enterprise Resource Planning (ERP) system, project management software, customer relationship management system, etc.The proper choice of critical success factors and metrics can improve the accuracy of new product evaluation, determine an optimal set of new products for development, m...

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Adaptive Neuro-Fuzzy Systems

Cited by 103 publications

References 48 publications

A Review on Classification of the Urban Poverty Using the Artificial Intelligence Method

A Review on Classification of the Urban Poverty Using the Artificial Intelligence Method

Performance Analysis of Conjugate Gradient Algorithms Applied to the Neuro-Fuzzy Feedback Linearization-Based Adaptive Control Paradigm for Multiple HVDC Links in AC/DC Power System

Estimating New Product Success with the Use of Intelligent Systems

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