Clustered defect detection of high quality chips using self-supervised multilayer perceptron
Expert Systems with Applications: An International Journal
Extracting symbolic knowledge from recurrent neural networks---A fuzzy logic approach
Fuzzy Sets and Systems
Neural network-based scalable fast intra prediction algorithm in H.264 encoder
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
A context layered locally recurrent neural network for dynamic system identification
Engineering Applications of Artificial Intelligence
Quantized Neural Modeling: Hybrid Quantized Architecture in Elman Networks
Neural Processing Letters
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We propose a new type of recurrent neural-network architecture, in which each output unit is connected to itself and is also fully connected to other output units and all hidden units. The proposed recurrent neural network differs from Jordan's and Elman's recurrent neural networks with respect to function and architecture, because it has been originally extended from being a mere multilayer feedforward neural network, to improve discrimination and generalization powers. We also prove the convergence properties of the learning algorithm in the proposed recurrent neural network, and analyze the performance of the proposed recurrent neural network by performing recognition experiments with the totally unconstrained handwritten numeric database of Concordia University, Montreal, Canada. Experimental results have confirmed that the proposed recurrent neural network improves discrimination and generalization powers in the recognition of visual patterns