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 ...

C networks, fuzzy networks and usefulness and applications. V technological re capable of rep1 experience. The concept underlining thei and comparisoi learning algorit architectures a r illustrated with identification, sc diagnosis tool, compression usii prediction, etc. In the later systems, includi Takagi-Sugano building blocks of fuzzy and ne] several applica concluded with chip. Fascination...

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)...

short-term prediction of traffic flow is central to alleviating congestion and controlling the negative impacts of environmental pollution resulting from vehicle emissions on both inter- and intra-urban highways. the strong need to monitor and control congestion time and costs for metropolis in developing countries has therefore motivated the current study. this paper establishes the applicatio...

improving time series forecastingaccuracy is an important yet often difficult task.both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. in this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series for...

This paper proposes a fuzzy inference based neural network for the forecasting of short term loads. The forecasting model is the integration of fuzzy inference engine and the neural network, known as Fuzzy Inference Neural Network (FINN). A FINN initially creates a rule base from existing historical load data. The parameters of the rule base are then tuned through a training process, so that th...