A hybrid intelligent approach for modeling brand choice and constructing a market response simulator

Kazemi, Seyed Mahmood; Hadavandi, Esmaeil; Mehmanpazir, Farhad; Nakhostin, Mohammad Masoud

doi:10.1016/j.knosys.2012.11.016

Cited by 13 publications

(7 citation statements)

References 42 publications

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“…Today, enormous attempts have been concentrated on artificial intelligence (AI) models for forecasting problems; meanwhile, applying AI models or an integration of various models have become an usual approach to increase forecasting precision [24,25]. Therefore, the literature on this topic has grown considerably.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparative Assessment of Predicting Daily Solar Radiation Using Bat Neural Network (BNN), Generalized Regression Neural Network (GRNN), and Neuro-Fuzzy (NF) System: A Case Study

et al. 2018

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Highly accurate estimating of daily solar radiation by developing an intelligent and robust model has been a subject of prominent concern for many researchers in the past few years. The precise prediction of solar radiation is of great interest and importance to improve the incorporation of solar power plants. In this study, a novel multilayer framework for a particular combination of the bat algorithm (BA) and neural networks (NN) is proposed, which is called bat neural network (BNN), aimed at predicting daily solar radiation over Iran. For appraising the performance of the proposed BNN, daily solar radiation data from four cities of Iran including Jask, Kermanshah, Ramsar, and Tehran are analyzed. The results indicate that among the tested models, BNN gains the best performance in the prediction of daily solar radiation. Among various soft computing approaches, the BA, which is inspired by the nature of microbats' behaviour, has a significant impact on the optimization of this study.

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Section: Literature Reviewmentioning

confidence: 99%

A Comparative Assessment of Predicting Daily Solar Radiation Using Bat Neural Network (BNN), Generalized Regression Neural Network (GRNN), and Neuro-Fuzzy (NF) System: A Case Study

et al. 2018

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“…As it is impressed by a series of variables and oscillating time series, the NG forecasting problem is a very challenging [8]. These days, massive efforts investigated artificial intelligence (AI) models or integration of several models (hybrid models) for prediction problems to increase the accuracy and the model reliability [9,10]. Also, numerous notable studies investigated by demand prediction for the case of energy resources [11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand

Hafezi¹,

Akhavan²,

Zamani³

et al. 2019

Preprint

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Recently natural gas (NG) global market attracted much attention in case it is cleaner than oil, and simultaneously in most regions is cheaper than renewable energy sources. However, price fluctuations, environmental concerns, technological development, emerging unconventional resources, energy security challenges, and shipment are some of the forces that made the NG market more dynamic and complex. From a policy-making perspective, it is vital to uncover demand-side future trends. This paper proposed an intelligent forecasting model to forecast NG global demand, however investigating a multi-dimensional purified input vector. The model starts with a data mining (DM) step to purify input features, identify the best time lags, and to pre-process selected input vector. Then a hybrid artificial neural network (ANN) which equipped with genetic optimizer is applied to set up ANN’s characteristics. Among 13 available input features, six features (e.g. Alternative and Nuclear Energy, CO2 Emissions, GDP per Capita, Urban Population, Natural Gas Production, Oil Consumption) selected as the most critical feature via the DM step. Then, the hybrid prediction model is designed to extrapolate the consumption of future trends. The proposed model overcomes competitive models refer to different error based evaluation statistics. Besides, as the model proposed the best input feature set, results compared to the model which used the raw input set, with no DM purification process.

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“…They have shown that PNN outperforms better, in higher accuracy and speed, than both ANN and Logistic model in customer choice model. Kazemi et al (2013) used a PNN trained by Dynamic Decay Adjustment Algorithm (DDA) to model customer choice. The evaluation process is carried out for modeling individual consumer choices in a retail coffee market.…”

Section: Introductionmentioning

confidence: 99%

Application of a Hybrid System of Probabilistic Neural Networks and Artificial Bee Colony Algorithm for Prediction of Brand Share in the Market

Shahrabi¹,

Khameneh²

2016

Industrial Engineering and Management Systems

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Manufacturers and retailers are interested in how prices, promotions, discounts and other marketing variables can influence the sales and shares of the products that they produce or sell. Therefore, many models have been developed to predict the brand share. Since the customer choice models are usually used to predict the market share, here we use hybrid model of Probabilistic Neural Network and Artificial Bee colony Algorithm (PNN-ABC) that we have introduced to model consumer choice to predict brand share. The evaluation process is carried out using the same data set that we have used for modeling individual consumer choices in a retail coffee market. Then, to show good performance of this model we compare it with Artificial Neural Network with one hidden layer, Artificial Neural Network with two hidden layer, Artificial Neural Network trained with genetic algorithms (ANN-GA), and Probabilistic Neural Network. The evaluated results show that the offered model is outperforms better than other previous models, so it can be use as an effective tool for modeling consumer choice and predicting market share.

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A hybrid intelligent approach for modeling brand choice and constructing a market response simulator

Cited by 13 publications

References 42 publications

A Comparative Assessment of Predicting Daily Solar Radiation Using Bat Neural Network (BNN), Generalized Regression Neural Network (GRNN), and Neuro-Fuzzy (NF) System: A Case Study

A Comparative Assessment of Predicting Daily Solar Radiation Using Bat Neural Network (BNN), Generalized Regression Neural Network (GRNN), and Neuro-Fuzzy (NF) System: A Case Study

Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand

Application of a Hybrid System of Probabilistic Neural Networks and Artificial Bee Colony Algorithm for Prediction of Brand Share in the Market

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