Fuzzy inference systems provide a simple yet effective solution to complex non-linear problems, which have been applied to numerous real-world applications with great success. However, conventional fuzzy inference systems may suffer from either too sparse, too complex or imbalanced rule bases, given that the data may be unevenly distributed in the problem space regardless of its volume. Fuzzy i...

A fuzzy controller is suited to control Antilock Brake System (ABS) however time complexity of fuzzy controller is high order. Problem Solution Data Structure (PSDS) has been introduced as a data-oriented model of fuzzy controller to reduce the response time. Locally learning of PSDS controller has been left as an open issue. Fuzzy Learning Mechanism (FLM) has been introduced by using fuzzy inf...

The fuzzy rule based inference is known to be a useful tool to capture the behavior of an approximate system in transportation. One of the obstacles of implementing the fuzzy rule based inference, however, has been to calibrate the membership functions of the fuzzy sets used in the rules. This paper proposes a way to calibrate the membership function when a set of input and output data is given...

in this paper, an adaptive neuro fuzzy sliding mode based genetic algorithm (anfsga) controlsystem is proposed for a ph neutralization system. in ph reactors, determination and control of ph isa common problem concerning chemical-based industrial processes due to the non-linearity observedin the titration curve. an anfsga control system is designed to overcome the complexity of precisecontrol o...

This paper proposs a systematic methodology of fuzzy logic modeling as a generic tool for modeling of complex systems. The methodology conveys three distinct features: 1) a unified parameterized reasoning formulation; 2) an improved fuzzy clustering algorithm; and 3) an efficient strategy of selecting significant system inputs and their membership functions. The reasoning mechanism introduces f...

Fuzziness and randomness are two distinct components of uncertainty. While fuzzy sets are a rigorous softening of random sets, many of the operations de ned in fuzzy logic lack a complete formalism, and are not strongly supported by experimental evidence. Causal Probabilistic Networks (CPN) or Bayesian networks provide an ultimately exible inference mechanism based on Bayesian probability princ...

in this research, the physico-chemical water quality parameters and the effect of climate changes onwater quality is evaluated. during the observation period (5 months) physico-chemical parameterssuch as water temperature, turbidity, saturated oxygen, dissolved oxygen, ph, chlorophyll-a, salinity,conductivity, and concentration of total nitrogen (nutrient level) as main pollutant factor have be...

Fuzzy cognitive map is a powerful modeling tool. It has several desirable properties on control. In this paper, we utilize the feature and the inference mechanism of fuzzy cognitive map, and present a control method, which study combines control theory with fuzzy cognitive map theory. The causal relationship of variables is constructed by online learning or offline learning, the values of contr...

It is shown that the Takagi-Sugeno-Kang (TSK) fuzzy inference mechanism is optimal with respect to a certain criterion that quantiies the controversy between the inference made, and the knowledge represented in the knowledge-base.

The main condition of the differently implicational inferencealgorithm is reconsidered from a contrary direction, which motivatesa new fuzzy inference strategy, called the double fuzzyimplications-based restriction inference algorithm. New restrictioninference principle is proposed, which improves the principle of thefull implication restriction inference algorithm. Furthermore,focusing on the ...