scholarly journals Intelligent Retail Forecasting System for New Clothing Products Considering Stock-out

2017 ◽  
Vol 25 (0) ◽  
pp. 10-16 ◽  
Author(s):  
He Huang ◽  
He Huang ◽  
Qiurui Liu
Keyword(s):  
Fuzzy Inference ◽  
Selection Process ◽  
New Products ◽  
Demand Forecasting ◽  
Fast Fashion ◽  
Inference System ◽  
Factors Affecting ◽  
Neuro Fuzzy ◽  
Wide Range ◽  
Forecasting System

Improving the accuracy of forecasting is crucial but complex in the clothing industry, especially for new products, with the lack of historical data and a wide range of factors affecting demand. Previous studies more concentrate on sales forecasting rather than demand forecasting, and the variables affecting demand remained to be optimized. In this study, a two-stage intelligent retail forecasting system is designed for new clothing products. In the first stage, demand is estimated with original sales data considering stock-out. The adaptive neuro fuzzy inference system (ANFIS) is introduced into the second stage to forecast demand. Meanwhile a data selection process is presented due to the limited data of new products. The empirical data are from a Canadian fast-fashion company. The results reveal the relationship between demand and sales, demonstrate the necessity of integrating the demand estimation process into a forecasting system, and show that the ANFIS-based forecasting system outperforms the traditional ANN technique.

Comparative Analysis of Artificial Neural Networks and Neuro-Fuzzy Models for Multicriteria Demand Forecasting

2013 ◽  
Vol 3 (1) ◽  
pp. 1-24 ◽  
Author(s):  
Golam Kabir ◽  
M. Ahsan Akhtar Hasin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fuzzy Systems ◽  
Fuzzy Inference ◽  
Demand Forecasting ◽  
Business Environment ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Model Complex ◽  
Artificial Neural

An organization has to make the right decisions in time depending on demand information to enhance the commercial competitive advantage in a constantly fluctuating business environment. Therefore, estimating the demand quantity for the next period most likely appears to be crucial. The objective of the paper is to propose a new forecasting mechanism which is modeled by artificial intelligence approaches including the comparison of both artificial neural networks (ANN) and adaptive network-based fuzzy inference system (FIS) techniques to manage the fuzzy demand with incomplete information. Artificial neural networks has been applied as it is capable to model complex, nonlinear processes without having to assume the form of the relationship between input and output variables. Neuro-fuzzy systems also utilized to harness the power of the fuzzy logic and ANNs through utilizing the mathematical properties of ANNs in tuning rule-based fuzzy systems that approximate the way human’s process information. The effectiveness of the proposed approach to the demand forecasting issue is demonstrated for a 20/25 MVA Distribution Transformer from Energypac Engineering Limited (EEL), a leading power engineering company of Bangladesh.

scholarly journals A-OLSR: ANFIS based OLSR to select Multi point relay

2019 ◽  
Vol 9 (1) ◽  
pp. 646
Author(s):  
Vivek Sharma ◽  
Bashir Alam ◽  
M. N. Doja
Keyword(s):  
Routing Protocol ◽  
Fuzzy Inference ◽  
Selection Process ◽  
Delivery Ratio ◽  
Network Simulator ◽  
Inference System ◽  
Link State ◽  
Neuro Fuzzy ◽  
Constant Bit Rate ◽  
Node Energy

<span lang="EN-IN">The characteristics like dynamic topology, power consumption, mobility etc. may leads to affect the routing process of packet as it progresses from one node to another node. The energy of each node is very limited in MANET’s due to which it becomes an important parameter to be considered while selecting the route. The ‘Optimized Link State Routing Protocol’ (OLSR) does not consider node energy during Multipoint relay (MPR) selection process. This paper proposes an improvement of OLSR routing protocol named as A-OLSR protocol using node energy during its MPR process. The improvement is based on adaptive neuro fuzzy inference system (ANFIS). The network simulator NS2.35 is used for the simulation, random way point model for mobility and constant bit rate (CBR) for traffic process. The performance of proposed A-OLSR protocol is evaluated using the packet delivery ratio (PDR) and end to end delay metrices. The simulation results prove the superiority of the proposed protocol in terms of PDR.</span>

scholarly journals ANFIS for Tamil Phoneme Classification

2019 ◽  
Vol 8 (6) ◽  
pp. 2907-2914
Keyword(s):  
Neural Networks ◽  
Markov Models ◽  
Fuzzy Inference ◽  
Hybrid Architecture ◽  
Phoneme Recognition ◽  
Inference System ◽  
Phoneme Classification ◽  
Neuro Fuzzy ◽  
Wide Range ◽  
Non Linear Systems

Phoneme recognition is an intricate problem lying under non-linear systems. Most research in this area revolve around try to model the pattern of features observed in the speech spectra with the use of Hidden Markov Models (HMM), various types of neural networks like deep recurrent neural networks, time delay neural networks, etc. for efficient phoneme recognition. In this paper, we study the effectiveness of the hybrid architecture, the Adaptive Neuro-Fuzzy Inference System (ANFIS) for capturing the spectral features of the speech signal to handle the problem of Phoneme Recognition. In spite of a wide range of research in this field, here we examine the power of ANFIS for least explored Tamil phoneme recognition problem. The experimental results have shown the ability of the model to learn the patterns associated with various phonetic classes, indicated with recognition improvement in terms of accuracy to its counterparts.

scholarly journals Predictive Modeling of Henry’s Law Constant in Chemical Structures Using LSSVM and ANFIS Algorithms

2020 ◽  
Author(s):  
Qikai Wang ◽  
Aiqin Yao ◽  
Manouchehr Shokri ◽  
Adrienn A. Dineva
Keyword(s):  
Chemical Structure ◽  
Fuzzy Inference ◽  
Low Cost ◽  
Support Vector ◽  
Inference System ◽  
Chemical Structures ◽  
Neuro Fuzzy ◽  
Wide Range ◽  
Accuracy Of Measurement

Henry&rsquo;s constants for different existing compounds in water have great importance in transfer calculations. Measurement of these constants face different difficulties including high costs of experiment and low accuracy of measurement apparatus. Due to these facts, proposing a low cost and accurate approach becomes highlighted. To this end, adaptive neuro-fuzzy inference system (ANFIS) and least squares support vector machine (LSSVM) have been used as Henry&rsquo;s constant predictor tools. The molecular structure of compounds has been used as inputs of models. After training the models, the visual and mathematical studies of outputs have been done. The coefficients of determination of LSSVM and ANFIS algorithms are 0.999 and 0.990 respectively. According to the comprehensiveness of databank and accurate prediction of algorithms, it can be concluded that LSSVM and ANFIS algorithms are accurate methods for prediction of Henry&rsquo;s constant in wide range of chemical structure of compounds in water.

Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam's shear strength

2016 ◽  
Vol 21 (3) ◽  
pp. 679-688 ◽  
Author(s):  
M. Safa ◽  
M. Shariati ◽  
Z. Ibrahim ◽  
A. Toghroli ◽  
Shahrizan Bin Baharom ◽  
...  
Keyword(s):  
Shear Strength ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Factors Affecting ◽  
Neuro Fuzzy

Tourism Demand Forecasting Based on a Neuro-Fuzzy Model

2014 ◽  
Vol 1 (1) ◽  
pp. 60-69 ◽  
Author(s):  
George Atsalakis ◽  
Eleni Chnarogiannaki ◽  
Consantinos Zopounidis
Keyword(s):  
Time Series ◽  
Fuzzy Inference ◽  
Demand Forecasting ◽  
Fuzzy Model ◽  
Time Series Models ◽  
Tourism Demand ◽  
Anfis Model ◽  
Inference System ◽  
Forecasting Models ◽  
Neuro Fuzzy

Tourism in Greece plays a major role in the country's economy and an accurate forecasting model for tourism demand is a useful tool, which could affect decision making and planning for the future. This paper answers some questions such as: how did the forecasting techniques evolve over the years, how precise can they be, and in what way can they be used in assessing the demand for tourism? An Adaptive Neuro-Fuzzy Inference System (ANFIS) has been used in making the forecasts. The data used as input for the forecasting models relates to monthly time-series tourist arrivals by air, train, sea and road into Greece from January 1996 until September 2011. 80% of the data has been used to train the forecasting models and the rest to evaluate the models. The performance of the model is achieved by the calculation of some well known statistical errors. The accuracy of the ANFIS model is further compared with two conventional forecasting models: the autoregressive (AR) and autoregressive moving average (ARMA) time-series models. The results were satisfactory even if the collected data were not pleasing enough. The ANFIS performed further compared to the other time-series models. In conclusion, the accuracy of the ANFIS model forecast proved its great importance in tourism demand forecasting.

Demand forecasting for apparel manufacturers by using neuro-fuzzy techniques

2014 ◽  
Vol 9 (1) ◽  
pp. 18-35 ◽  
Author(s):  
Asli Aksoy ◽  
Nursel Öztürk ◽  
Eric Sucky
Keyword(s):  
Fuzzy Inference ◽  
Demand Forecasting ◽  
Real Life ◽  
Learning Capability ◽  
Inference System ◽  
Content Type ◽  
Literature Research ◽  
Forecasting System ◽  
Fuzzy Techniques ◽  
Apparel Manufacturers

Purpose – According to literature research and conversations with apparel manufacturers' specialists, there is not any common analytic method for demand forecasting in apparel industry and to the authors' knowledge, there is not adequate number of study in literature to forecast the demand with adaptive network-based fuzzy inference system (ANFIS) for apparel manufacturers. The purpose of this paper is constructing an effective demand forecasting system for apparel manufacturers. Design/methodology/approach – The ANFIS is used forecasting the demand for apparel manufacturers. Findings – The results of the proposed study showed that an ANFIS-based demand forecasting system can help apparel manufacturers to forecast demand accurately, effectively and simply. Originality/value – ANFIS is a new technique for demand forecasting, combines the learning capability of the neural networks and the generalization capability of the fuzzy logic. In this study, the demand is forecasted in terms of apparel manufacturers by using ANFIS. The input and output criteria are determined based on apparel manufacturers' requirements and via literature research and the forecasting horizon is about one month. The study includes the real-life application of the proposed system, and the proposed system is tested by using real demand values for apparel manufacturers.

scholarly journals Integrated artificial intelligence algorithm for skin detection

2018 ◽  
Vol 16 ◽  
pp. 02004 ◽  
Author(s):  
Idoko John Bush ◽  
Rahib Abiyev ◽  
Mohammad Khaleel Sallam Ma’aitah ◽  
Hamit Altıparmak
Keyword(s):  
Fuzzy Inference ◽  
Data Mapping ◽  
Colour Space ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Wide Range ◽  
Input Dataset ◽  
Fcm Clustering ◽  
Artificial Intelligence Algorithm ◽  
Fuzzy Network

The detection of skin colour has been a useful and renowned technique due to its wide range of application in both analyses based on diagnostic and human computer interactions. Various problems could be solved by simply providing an appropriate method for pixel-like skin parts. Presented in this study is a colour segmentation algorithm that works directly in RGB colour space without converting the colour space. Genfis function as used in this study formed the Sugeno fuzzy network and utilizing Fuzzy C-Mean (FCM) clustering rule, clustered the data and for each cluster/class a rule is generated. Finally, corresponding output from data mapping of pseudo-polynomial is obtained from input dataset to the adaptive neuro fuzzy inference system (ANFIS).

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