this paper proposes a neuro fuzzy model for analyzing the relationship between contractor’s qualifications and project quality in research projects. the proposed model has been implemented in a research-based organization, iies. cross validation method has been used in order to generate some set of data which have been used for different evaluations. the proposed neuro fuzzy model has dominated...

The implementation of a Neuro-Fuzzy nonlinear adaptive structure, with Local Linear Models (LLM), designed for fuel pressure estimation in diesel common-rail (CR) hydraulic system, represents the main topic. Hydraulic systems, in general, are nonlinear and engineers have often struggled to find the best solution to approximate the input-output dependencies. Powerful tools are necessary for spli...

We describe a simple model of spike processing build upon a number of neural hardware primitives including integrate-and-fire neurons, passive dendritic trees, simple integrators, inhibition logic and one-to-many axonal/dendritic tree connectivity. Functionally, our model of spike processing consists of neuro-modulators, communication channels, neuro-demodulators and filters. Integrate-and-fire...

A simple neuro-controlle r for a synchronous generator is presented in this paper. The controller performs the function of the terminal voltage control. By representing the proposed neuro-controller in s-domain, its parameters to ensure system stability can be obtained analytically. Results of simulation studies on a non-linear seventh order generator model with the neuro-controller using calcu...

Linear Independent Component Analysis (ICA) has become an important technique in unsupervised neural learning. Even though linear ICA yields meaningful results in many cases, it can provide a crude approximation only for general nonlinear data distributions. In this paper we study techniques where local ICA models are applied to data rst grouped or clustered using some suitable algorithm. The g...

Embed-to-control (E2C) [17] is a model for solving high-dimensional optimal control problems by combining variational autoencoders with locally-optimal controllers. However, the current E2C model suffers from two major drawbacks: 1) its objective function does not correspond to the likelihood of the data sequence and 2) the variational encoder used for embedding typically has large variational ...