This paper develops a design method of recurrent fuzzy neural network (RFNN) control system for multi-input multi-output (MIMO) nonlinear dynamic systems. This control system consists of a state feedback controller and an RFNN controller. The state feedback controller is a basic stabilizing controller to stabilize the system, and the RFNN controller presents a robust controller to deal with unc...

Recurrent neural networks have recently been demonstrated to have the ability to learn simple grammars. In particular, networks using second-order units have been successfully at this task. However, it is often difficult to predict the optimal neural network size to induce an unknown automaton from examples. Instead of just adjusting the weights in a network of fixed topology, we adopt the dyna...

An adaptive software reliability prediction model using evolutionary connectionist approach based on Recurrent Radial Basis Function architecture is proposed. Based on the currently available software failure time data, Fuzzy Min-Max algorithm is used to globally optimize the number of the k Gaussian nodes. The corresponding optimized neural network architecture is iteratively and dynamically r...

This paper presents two innovative evolutionary-neural systems based on feed-forward and recurrent neural networks used for quantitative analysis. These systems have been applied for approximation of phenol concentration. Their performance was compared against the conventional methods of artificial intelligence (artificial neural networks, fuzzy logic and genetic algorithms). The proposed syste...

In this paper, a hardware implementation of a recurrent neural fuzzy network (RNFN) used for identification and prediction is proposed. A recurrent network is embedded in the RNFN by adding feedback connections in the second layer, where the feedback units act as memory elements. Although the back propagation (BP) learning algorithm is widely used in the RNFN, BP is too complicated to be implem...

A simple probability based term weighting scheme for automated text classification p. 33 Text classification for healthcare information support p. 44 Nurse scheduling using fuzzy multiple objective programming p. 54 Fuzzy adaptive threshold determining in the key inheritance based sensor networks p. 64 A new approach for evaluating students' answerscripts based on interval-valued fuzzy sets p. ...

to many temperature analyses as a result of its simplicity. In [2], a PID controller is used for temperature control of a 20 KW industrial electric resistance furnace where the plant is modeled by a first-order transfer function with a time delay. Temperature control of a heated barrel with electric heaters and water coolers was proposed in [3] which used the discrete-time variable structure sy...

The RPROP algorithm was originally developed in [5] for static networks and constitutes one of the best performing first order learning methods for neural networks [6]. However, in RPROP the problem of poor convergence to local minima, faced by all gradient descent-based methods, is not fully eliminated. Hence, in an attempt to alleviate this drawback, a combination of RPROP with the global sea...

etiologic factors for recurrent miscarriage (rm) include autoimmune diseases, the most frequently antiphospholipid syndrome and thyroiditis. some women who suffer from rm might also have an altered immune system. we aimed to evaluate possible associations between anti-thyroid and anti-phospholipid antibodies in women with rm. in a retrospective case series 1 on 156 women with rm, major outcome ...

The problem of short-term electric load forecasting (STLF) is considered. A modified architecture of Elman-type recurrent neural network is proposed. It utilizes a special fuzzification layer to deal with quantitative as well as ordinal and nominal data. The second hidden layer of the network consists of standard Rosenblatt-type neurons with sigmoidal activation functions. The context layer is ...