in this study two intelligent systems, based on adaptive neuro-fuzzy inference systems (anfis) and artificial neural networks (anns) of forecasting municipal solid wastes (msw) generation has been proposed. anfis and anns as an intelligent tool compared with together was used to monthly prediction of msw generated in tehran. monthly amount of solid wastes (sw), total monthly precipitation, mont...

Recent studies have shown that both Mamdani-type and Takagi-Sugeno-type fuzzy systems are universal approximators in that they can uniformly approximate continuous functions defined on compact domains with arbitrarily high approximation accuracy. In this paper, we investigate necessary conditions for general multiple-input single-output (MISO) Mamdani fuzzy systems as universal approximators wi...

It is firstly proved that the multi-input-single-output (MISO) fuzzy systems based on interval-valued $R$- and $S$-implications can approximate any continuous function defined on a compact set to arbitrary accuracy.  A formula to compute the lower upper bounds on the number  of interval-valued fuzzy sets needed to achieve a pre-specified approximation  accuracy for an arbitrary multivariate con...

In this paper, we present a new approach for extracting fuzzy rules from numerical input–output data for pattern classification. The approach combines the merits of the fuzzy logic theory, and neural networks. The proposed approach uses rapid back propagation neural network (RBPNN), which can handle both quantitative (numerical) and qualitative (linguistic) knowledge. The network can be regarde...

Load sensor is developed using mamdani fuzzy inference system and sugeno fuzzy inference system. It is two input and one output sensor. Both mamdani-type fuzzy inference system and sugeno-type fuzzy inference system are simulated using MATLAB fuzzy logic toolbox. This paper outlines the basic difference between these two fuzzy inference system and their simulated results are compared. Index Ter...

In many modelling problems, there is some inherent monotone relationship between one or more of the input variables and the output variable. We consider the prototypical case of an increasing relationship between each of the input variables and the output variable. When using fuzzy rule-based models, this desired monotonicity is reflected in the rule base, given an appropriate ordering on the f...

Data Envelopment Analysis (DEA) is a widely used mathematical programming technique for comparing the inputs and outputs of a set of homogenous Decision Making Units (DMUs) by evaluating their relative efficiency. The conventional DEA methods assume deterministic and precise values for the input and output observations. However, the observed values of the input and output data in real-world pro...

Fuzzy control systems have had various applications in a wide range of science and engineering in recent years. Since an unstable control system is typically useless and potentially dangerous, stability is the most important requirement for any control system (including fuzzy control system). Conceptually, there are two types of stability for control systems: Lyapunov stability (a special case ...

Fuzzy modeling has many advantages over the non-fuzzy methods, such as robustness against uncertainties and less sensitivity to the varying dynamics of nonlinear systems. Data-driven fuzzy modeling needs to extract fuzzy rules from the input/output data, and train the fuzzy parameters. This paper takes advantages from deep learning, probability theory, fuzzy modeling, and extreme learning machi...