A Radial Basis Function ( RBF ) neural network can be regarded as a feed forward network composed of multiple layers of neurons with entirely different roles. The input layer made of sensory units that connect the network to its environment. A radial basis function neural network depends mainly upon an adequate choice of the number and positions of its basis function centers. In case of general...

Transformer benefits from the high parallelization of attention networks in fast training, but it still suffers slow decoding partially due to linear dependency O(m) decoder self-attention on previous target words at inference. In this paper, we propose a generalized average network (AAN+) aiming speeding up by reducing O(1). We find that learned weights follow some patterns which can be approx...

Modern Convolutional Neural Network (CNN) architectures, despite their superiority in solving various problems, are generally too large to be deployed on resource constrained edge devices. In this paper, we reduce memory usage and floating-point operations required by convolutional layers CNNs. We compress these generalizing the Kronecker Product Decomposition apply multidimensional tensors, le...

information on nitrate in groundwater resources requires periodic measurements are accurate. despite the measure in some areas due to sensitive social and health community are not reported. therefore, be informed of the status of each area of water quality, modeling is essential. the purpose of this study was the application of artificial neural network method for estimating nitrate and compare...

In this brief, a new neural network model called generalized adaptive resonance theory (GART) is introduced. GART is a hybrid model that comprises a modified Gaussian adaptive resonance theory (MGA) and the generalized regression neural network (GRNN). It is an enhanced version of the GRNN, which preserves the online learning properties of adaptive resonance theory (ART). A series of empirical ...

in this paper, a recurrent fuzzy-neural network (rfnn) controller with neural network identifier in direct control model is designed to control the speed and exhaust temperature of the gas turbine in a combined cycle power plant. since the turbine operation in combined cycle unit is considered, speed and exhaust temperature of the gas turbine should be simultaneously controlled by fuel command ...

the paper deals with data envelopment analysis (dea) and artificial neural network (ann). we believe that solving for the dea efficiency measure, simultaneously with neural network model, provides a promising rich approach to optimal solution. in this paper, a new neural network model is used to estimate the inefficiency of dmus in large datasets.

In an attempt to enhance the neural network technique so that it can evolve from a ‘‘black box” tool into a semi-analytical one, we propose a novel modeling approach of imposing ‘‘generalized constraints” on a standard neural network. We redefine approximation problems by use of a new formalization with the aim of embedding prior knowledge explicitly into the model to the maximum extent. A gene...