The development of a weighted evolving fuzzy neural network for PCB sales forecasting

Chang, Pei‐Chann; Wang, Yen-Wen; Liu, Chen-Hao

doi:10.1016/j.eswa.2005.11.021

Cited by 142 publications

(94 citation statements)

References 15 publications

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“…Since statistical models could not give satisfying results, artificial intelligence algorithms were tried in numerous studies. For instance, Neural Network algorithms were commonly applied in the literature [6][7][8][9][10][11]. Given studies provided impressive results with NN algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Pattern Forecasting from Banking Financial Data

Haque¹,

Mottakin²,

Nahar³

2018

IJCA

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Section: Literature Reviewmentioning

confidence: 99%

Automatic Pattern Forecasting from Banking Financial Data

Haque¹,

Mottakin²,

Nahar³

2018

IJCA

View full text Add to dashboard Cite

“…Thus, artificial intelligence models were started to use. Especially, Neural Networks were used in a large number of works [11]- [16]. Some works compared Neural Networks with traditional statistical models and showed that Neural Networks provides better results than traditional statistical models.…”

Section: Related Workmentioning

confidence: 99%

A Decision Support System for Demand Forecasting based on Classifier Ensemble

Islek

Öğüdücü

2017

Annals of Computer Science and Information Systems

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Abstract-Demand forecasting is the process of constructing forecasting models to estimate the quantities of several products that customers will purchase in the future. When the warehouse and the number of products grow, forecasting the demand becomes dramatically hard. Most of the demand forecasting models rely on a single classifier or a simple combination of these models. In order to improve demand forecasting accuracy, we investigate several different classifiers such as MLP, Bayesian Network, Linear Regression and SVM analyzing their accuracy and performance. Moreover, we also studied some classifier combination techniques by approaching from demand forecasting perspective. In this paper, we propose a methodology to combine various forecasting models using neural networks rather for supporting demand forecasting. The proposed methodology is tested against single classifiers and classifier ensemble models using a real dataset. Experiments indicate that the proposed methodology outperforms all the other single classifiers tested in this study and their simple combinations.

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“…They found that performance of the model was superior to previous methods that proposed for PCB sales forecasting. Chang et al (2007) developed a Weighted Evolving Fuzzy Neural Network (WEFuNN) model for PCB sales forecasting. The proposed model was based on combination of sales key factors selected using GRA.…”

Section: Pcb Sales Forecastingmentioning

confidence: 99%

A Novel Approach Based on Genetic Fuzzy Clustering and Adaptive Neural Networks for Sales Forecasting

Hichama

Mohameda

Abdellahb

2013

Journal of Computer Science

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This article proposes a new hybrid sales forecasting system based on genetic fuzzy clustering and BackPropagation (BP) Neural Networks with adaptive learning rate (GFCBPN).The proposed architecture consists of three stages: (1) utilizing Winter's Exponential Smoothing method and Fuzzy C-Means clustering, all normalized data records will be categorized into k clusters; (2) using an adapted Genetic Fuzzy System (MCGFS), the fuzzy rules of membership levels to each cluster will be extracted; (3) each cluster will be fed into parallel BP networks with a learning rate adapted as the level of cluster membership of training data records. Compared to previous researches which use Hard clustering, this research uses the fuzzy clustering which capable to increase the number of elements of each cluster and consequently improve the accuracy of the proposed forecasting system. Printed Circuit Board (PCB) will be utilized as a case study to evaluate the precision of our proposed system. Experimental results show that the proposed model outperforms the previous and traditional approaches. Therefore, it is a very promising method for financial forecasting.

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The development of a weighted evolving fuzzy neural network for PCB sales forecasting

Cited by 142 publications

References 15 publications

Automatic Pattern Forecasting from Banking Financial Data

Automatic Pattern Forecasting from Banking Financial Data

A Decision Support System for Demand Forecasting based on Classifier Ensemble

A Novel Approach Based on Genetic Fuzzy Clustering and Adaptive Neural Networks for Sales Forecasting

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