Sales forecasting using extreme learning machine with applications in fashion retailing
Decision Support Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Hybrid computational models for the characterization of oil and gas reservoirs
Expert Systems with Applications: An International Journal
Incremental modeling with rough and fine tuning method
Applied Soft Computing
New model for system behavior prediction based on belief rule based systems
Information Sciences: an International Journal
Intelligent fabric hand prediction system with fuzzy neural network
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An intelligent fast sales forecasting model for fashion products
Expert Systems with Applications: An International Journal
A Generalized Ellipsoidal Basis Function Based Online Self-constructing Fuzzy Neural Network
Neural Processing Letters
An empirical study of intelligent expert systems on forecasting of fashion color trend
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Neuro-fuzzy integrated system with its different domain applications
International Journal of Intelligent Systems Technologies and Applications
Application of BW-ELM model on traffic sign recognition
Neurocomputing
Fast fashion sales forecasting with limited data and time
Decision Support Systems
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This paper investigates the feasibility of applying a relatively novel neural network technique, i.e., extreme learning machine (ELM), to realize a neuro-fuzzy Takagi-Sugeno-Kang (TSK) fuzzy inference system. The proposed method is an improved version of the regular neuro-fuzzy TSK fuzzy inference system. For the proposed method, first, the data that are processed are grouped by the k-means clustering method. The membership of arbitrary input for each fuzzy rule is then derived through an ELM, followed by a normalization method. At the same time, the consequent part of the fuzzy rules is obtained by multiple ELMs. At last, the approximate prediction value is determined by a weight computation scheme. For the ELM-based TSK fuzzy inference system, two extensions are also proposed to improve its accuracy. The proposed methods can avoid the curse of dimensionality that is encountered in backpropagation and hybrid adaptive neuro-fuzzy inference system (ANFIS) methods. Moreover, the proposed methods have a competitive performance in training time and accuracy compared to three ANFIS methods.