Optimization of machining parameters is very important and the main goal in every machining process. Surface finishing prediction is a pre-requirement to establish a center for automatic machining operations. In this research, a neuro-fuzzy approach is used in order to model and predict the surface roughness in dry turning. This approach has both the learning capability of neural network and li...

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...

allocation and removing of excess water from the irrigation and drainage network is one of the most important activities in the management of these networks. side weir is one of the most common structures for this purpose. study on the flow hydraulic characteristics of this structure included two parts, defining the water surface profiles and estimating the discharge coefficient. to estimate th...

This paper proposes a neural network for building and optimizing fuzzy models. The network can be regarded both as an adaptive fuzzy inference system with the capability of learning fuzzy rules from data, and as a connectionist architecture provided with linguistic meaning. Fuzzy rules are extracted from training examples by a hybrid learning scheme comprised of two phases: rule generation phas...

In this research a fuzzy neural network is proposed so, fuzzy mechanism and adaptive neuro fuzzy mechanism are designed and simulated to control the (flow rate) control action on cell multiplexing in (ATM). The cell flow rate on the output of neuralfuzzy controller. Has been simulated depending on the input variables, one of these inputs is the queuing message (message length), the second one i...

Nonlinear Adaptive Noise Cancellation for 2-D Signals with Adaptive Neuro-Fuzzy Inference Systems Hao Qin Advisor: University of Guelph, 2004 Professor Simon X. Yang Neuro-fuzzy systems are capable of inducing rules from observations, where the adaptive neuro-fuzzy inference system (ANFIS) is an effective method that can be applied to a variety of domains such as pattern recognition, robotics, ...

This paper pursues some applications of Rough Set Theory (RST) and neural-fuzzy model to analysis of "lugeon data". In the manner, using Self Organizing Map (SOM) as a pre-processing the data are scaled and then the dominant rules by RST, are elicited. Based on these rules variations of permeability in the different levels of Shivashan dam, Iran has been highlighted. Then, via using a combining...

in recent years, use of fuzzy collection theories for modeling of hydrological phenomenon's that is including complexity and uncertainly is considered scholars. so in this research, adaptive neuro-fuzzy inference system (anfis) is used for performance of river flow forecasting process. in this research, three parameters such as raining, temperature and daily discharge of lighvanchai basin ...