An expert system of price forecasting for used cars using adaptive neuro-fuzzy inference

Wu, Jian-Da; Hsu, Chuang-Chin; Chen, Hui-Chu

doi:10.1016/j.eswa.2008.11.019

Cited by 81 publications

(32 citation statements)

References 19 publications

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“…The ANFIS utilizes the ANN training process in order to adjust the membership function and the associated parameter that approaches the desired data sets. The learning algorithm of the ANFIS is a hybrid learning algorithm comprising the use of the “back‐propagation learning algorithm” and “least squares method” together . In order to understand and simplify the process, a sample including two inputs and an output is considered.…”

Section: Methodsmentioning

confidence: 99%

Membership function comparative investigation on productivity forecasting of solar still using adaptive neuro‐fuzzy inference system approach

Mashaly

Alazba

2017

Env Prog and Sustain Energy

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Modeling solar still productivity (SSP) is one of the most studied topics in solar desalination due to it having essential applications in the design of solar still systems. This study applied an adaptive neuro-fuzzy inference system (ANFIS) and different membership functions (MFs) to predict the SSP required by designers, operators, and beneficiaries of solar stills. The output of this research can be used as a reference for designing and managing solar stills that could lead to optimizing the performance. The modeling process was based on real-field experimental data. The model considers the solar radiation, relative humidity, total dissolved solids of the feed, total dissolved solids of the brine, and feed flow rate as the input variables. The results show that ANFIS forecasting with generalized bell MF (GBELLMF) produced the highest correlation coefficient (CC) and the smallest root mean square error (RMSE) when compared with other MF types. Thus, the ANFIS model with GBELLMF (CC 5 0.99; RMSE 5 0.03 L/m 2 /h) provides the best SSP prediction accuracy, which is better than other models with MFs. In addition, the statistical indicators demonstrate that the ANFIS model is better for predicting the SSP than multiple linear regressions. These findings demonstrate that ANFIS can be applied to forecast the SSP using weather and operational data as inputs with the best membership function (which is GBELLMF).

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Section: Methodsmentioning

confidence: 99%

Membership function comparative investigation on productivity forecasting of solar still using adaptive neuro‐fuzzy inference system approach

Mashaly

Alazba

2017

Env Prog and Sustain Energy

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“…A lot of research has been carried out to study the strengths of neuro-fuzzy networks. In two separate works Wu [23] and Ying-M ing Wang [24] verified that ANFIS model co mprising the neural network's adaptive capability and the fu zzy logic's qualitative approximat ion outperforms artificial neural networks. Arora et al [3] studied the risk of not taking health insurance and developed ANFIS model for determining the degree to wh ich an insurance seeker is exposed to risk if he fails to take insurance.…”

Section: Proposed Methodologies For Estimation and Approximat Ionmentioning

confidence: 99%

Estimation and Approximation Using Neuro-Fuzzy Systems

Arora¹,

Saini²

2016

IJISA

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Abstract-Estimat ion and Approximation plays an important role in p lanning for future. People especially the business leaders, who understand the significance of estimation, practice it very often. The act of estimation or approximation involves analyzing historical data pertaining to domain, current trends and expectations of people connected to it. Exercising estimation is not only complicated due to technological change in the world around, but also due to complexity o f the problems. Traditional nu merical based techniques for solution of illdefined non-linear real world problems are not sufficient. Hence, there is a need of some robust methodologies which can deal with dynamic environment, imprecise facts and uncertainty in the available data to achieve practical applicability at low cost. Soft computing seeks to solve class of problems not suited for traditional algorithmic approaches. To address the common problems in business of inexactness, some models are put forward for servicing, support and monitoring by approximating and estimat ing important outcomes. This work illustrates some very general yet widespread problems which are of interest to common people. The suggested approaches can overcome the fuzziness in tradit ional methods by predicting some future events and getting better control on business. This includes study of various neuro-fu zzy arch itectures and their possible applicat ions in various areas, where decision-making using classical methods fail.

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“…Se ha utilizado otras técnicas de minería de datos relacionados al alquiler de vehículos como el uso de máquinas de vectores de soporte [9], construcción de sistemas expertos para predecir los precios en el negocio de los vehículos, utilizando inferencia neuro-difusa adaptativa [10], entre otros para el soporte de decisiones en el alquiler de vehículos [11].…”

Section: Trabajos Relacionadosunclassified

Predicción para el Negocio de Alquiler de Automóviles con Técnicas Supervisadas

Zapata-Quentasi

Yauri-Ituccayasi

Huamani-Avendaño

et al. 2019

Proceedings of the 17th LACCEI International Multi-Conference for Engineering, Education, and Technology: “Industry, Innovation

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Car rental is a new trend and is already a reality in many countries, as it is a cheaper option than maintaining your own. The objective of this article is to identify the ideal car for a person, according to the characteristics that you want. In the present work, a study was made of the previous steps involved in the prediction of a car according to the desired characteristics and a comparison of the classification algorithms was carried out to determine which classification is appropriate in terms of the accuracy of the prediction. The steps followed were: Data collection, preprocessing, data preparation and comparison of classification algorithms. The results obtained show that the Random Forest algorithm presents a 95.12% correct classification of the instances and a mean square error of 0.12, which are acceptable results for the tests performed.

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An expert system of price forecasting for used cars using adaptive neuro-fuzzy inference

Cited by 81 publications

References 19 publications

Membership function comparative investigation on productivity forecasting of solar still using adaptive neuro‐fuzzy inference system approach

Membership function comparative investigation on productivity forecasting of solar still using adaptive neuro‐fuzzy inference system approach

Estimation and Approximation Using Neuro-Fuzzy Systems

Predicción para el Negocio de Alquiler de Automóviles con Técnicas Supervisadas

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