Neurofuzzy systems—the combination of artificial neural networks with fuzzy logic—have become useful in many application domains. However, conventional neurofuzzy models usually need enhanced representational power for applications that require context and state (e.g., speech, time series prediction, control). Some of these applications can be readily modeled as finite state automata. Previousl...

This paper presents a novel QoS support approach for mobile ad hoc networks (MANET). The fuzzy logic is used for QoS management in two phases, first for traffic rate controlling, and then for packet accepting controlling. After using fuzzy logic we used Radial Basis Function Neural Networks (RBFNN), for QoS management. The results show that our new management methods (RBFNN QoS management) not ...

Neural networks, which make no assumption about data distribution, have achieved improved image classification results compared to traditional methods. Unfortunately, a neural network is generally perceived as being a ‘black box’. It is extremely difficult to document how specific classification decisions are reached. Fuzzy systems, on the other hand, have the capability to represent classifica...

A method for response integration in modular neural networks with type-2 fuzzy logic for biometric systems p. 5 Evolving type-2 fuzzy logic controllers for autonomous mobile robots p. 16 Adaptive type-2 fuzzy logic for intelligent home environment p. 26 Interval type-1 non-singleton type-2 TSK fuzzy logic systems using the hybrid training method RLS-BP p. 36 An efficient computational method to...

Critical care providers are faced with resource shortages and must find ways to effectively plan their resource utilization. Neural networks provide a new method for evaluating trauma patient (and other medical patient) level of illness and accurately predicting a patient’s length of stay at the critical care facility. Backpropagation, radial-basis-function, and fuzzy ARTMAP neural networks are...

The digital hardware implementation of various fuzzy operations furthermore of fuzzy flip-flops has been the subject of intense study and application. The fuzzy D flip-flop derived from fuzzy J-K one is a single input single output unit with sigmoid transfer characteristics in some particular cases, proper to use as neuron in a Fuzzy Neural Networks (FNN). In this paper we propose the hardware ...

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This paper investigates function approximation on discrete input spaces by both neural networks and neural-fuzzy systems. Rather than use existing neural networks for function approximation on continuous input spaces, this paper proposes, based on a hierarchical systematic perspective, four simplified approximation schemes: simplified neural networks, extended simplified neural networks, simple...

This paper implements a Neuro-Fuzzy (FNN) approach to autonomously navigate a car-like robot in an unknown environment. The applied technique allows the robot to avoid obstacles and locally search for a path leading to the goal after learning and adaptation. It is based on two Fuzzy Artmap neural networks, a Reinforcement trial and error neural network and a Mamdani fuzzy logic controller (FLC)...