Using data-fuzzification technology in small data set learning to improve FMS scheduling accuracy

Abstract

Production decisions in real dynamic flexible manufacturing systems (FMS), especially in the early stages are often made with limited information. Information is limited because scheduling knowledge is hard to establish in such an environment. Though the machine learning technique in the field of Artificial Intelligence is thus used for this task by many researchers, this research is aimed at increasing the accuracy of machine learning for FMS scheduling using small data sets. Approaches used include data-fuzzifying, domain range expansion, and the application of adaptive-network-based fuzzy inference systems (ANFIS). The results indicate that learning accuracy under this strategy is significantly better than that of a traditional crisp data neural networks.

A comparative assessment of classification methods for resonance frequency prediction of Langevin piezoelectric transducers

“…The mega-fuzzification method is based on neuro-fuzzy and is designed to improve the learning accuracy when data size is small [13][14][15][16]. Although mega-fuzzification is based on FNN for improving accuracy in small data set learning, it applies FNN using a different concept [13][14][15][16].…”

“…The basic model of the ANFIS is Sugeno fuzzy model [13][14][15]. In the model, assuming x and y are two input fuzzy sets and z is the output fuzzy set, the fuzzy if-then rules for the Sugeno fuzzy model is formatted as: If x is A and y is B then z = f(x, y) Consider two rules of a first-order Sugeno fuzzy model, the if-then rules can be:…”

Utilizing an Adaptive Grey Model for Short-Term Time Series Forecasting: A Case Study of Wafer-Level Packaging

“…Table 2. (1,1) Model. To assess the reasonableness and applicability of the adaptive grey forecasting model in the early stage of the WLP process, we first use four different measurements that are often used in pretesting modeling fitness in the grey system theory [20] to evaluate the forecasting performance.…”

“…Li and Yeh [19] proposed the trend and potency tracking method (TPTM), which is an analysis method that uses the characteristics of data to explore possible changes in data behavior in different stages of a process, and this is the key concept used in AGM (1,1) to improve the accuracy of the conventional grey forecasting model.…”

“…Firms can gain competitive advantages by more effectively controlling their manufacturing systems [1]. In this context, the early stages of a manufacturing system are especially important for managers, because any slight change in the parameters used at this time will significantly influence final product quality and manufacturing performance [2].…”