This paper presents a biologically plausible mechanism of back-propagating network output error to previous layers of processing in a particular multi-layer neural network. This mechanism is used in a network that is designed to mimic familiarity discrimination as performed by the perirhinal cortex of the temporal lobe. In the algorithm, the error of the network during an initial classification...

Classification of heart arrhythmia is an important step in developing devices for monitoring the health of individuals. This paper proposes a three module system for classification of electrocardiogram (ECG) beats. These modules are: denoising module, feature extraction module and a classification module. In the first module the stationary wavelet transform (SWF) is used for noise reduction of ...

In this paper, a gradient-based back propagation dynamical iterative learning algorithm is proposed for structure optimization and parameter tuning of the neuro-fuzzy system. Premise and consequent parameters of the neuro-fuzzy model are initialized randomly and then tuned by the proposed iterative algorithm. The learning algorithm is based on the first order partial derivative of the output wi...

The back-propagation neural network algorithm is a commonly used method for predicting the secondary structure of proteins. Whilst popular, this method can be slow to learn and here we compare it with an alternative: the cascade-correlation architecture. Using a constructive algorithm, cascade-correlation achieves predictive accuracies comparable to those obtained by back-propagation, in shorte...

In this paper we present a novel algorithm to learn a score distribution over the nodes of a labeled graph (directed or undirected). Markov Chain theory is used to define the model of a random walker that converges to a score distribution which depends both on the graph connectivity and on the node labels. A supervised learning task is defined on the given graph by assigning a target score for ...

Back-propagation algorithm is one of the most widely used and popular techniques to optimize the feed forward neural network training. Nature inspired meta-heuristic algorithms also provide derivative-free solution to optimize complex problem. Artificial bee colony algorithm is a nature inspired meta-heuristic algorithm, mimicking the foraging or food source searching behaviour of bees in a bee...

In this paper, an efficient face recognition system based on sub-window extraction algorithm and recognition based on principal component analysis (PCA) and Back propagation algorithm is proposed. Our proposed method works on two phases: Extraction phase and Recognition phase. In extraction phase, face images are captured from different sources and then enhanced using filtering, clipping and hi...

Signal processing algorithms such as neural network learning, convolution, cross-correlation, IIR ltering, etc., can be computationally time-consuming and are often used in time-critical application. This makes it desirable to achieve high e ciency on these routines. Such algorithms are often coded in assembly language to achieve optimal speed, but it is then di cult to make a full exploration ...