This paper proposes a new hybrid time series forecasting system which is the fusion of the fuzzy system and the artificial neural network. The proposed fuzzy-neural system consists of 5 layers: the input layer, the fuzzification layer, the inference layer, the hidden layer, and the output layer. The artificial neural network is used as the fuzzy inference engine, while the genetic algorithm is ...

Disease diagnosis is a challenging task due to large number of associated factors. Uncertainty in the process arises from inaccuracy patient attributes, missing data, and limitation medical expert's ability define cause effect relationships when there are multiple interrelated variables. This paper aims demonstrate an integrated view deploying smart disease using Internet Things (IoT) empowered...

This paper presents a new general purpose neuro-fuzzy controller to realize adaptive-network-based fuzzy inference system (ANFIS) architecture. ANFIS which tunes the fuzzy inference system with a back propagation algorithm based on collection of input-output data makes fuzzy system to learn. To implementing this idea we propose several improved CMOS analog circuits, including Gaussian-like memb...

this study investigates the oil extraction from pistacia khinjuk by the application of enzyme.artificial neural network (ann) and adaptive neuro fuzzy inference system (anfis) were applied formodeling and prediction of oil extraction yield. 16 data points were collected and the ann was trained with onehidden layer using various numbers of neurons. a two-layered ann provides the best results, us...

Fuzzy inference control uses fuzzy sets to describe the antecedents and consequents of If-Then rules. However, most surveys show the antecedents and consequents are uncertain sets rather than fuzzy sets. This fact provides a motivation to invent an uncertain inference control method. This paper gives an introduction to the design procedures of uncertain inference controller. As an example, an u...