Introduction to artificial neural systems
Introduction to artificial neural systems
Building neural networks
Artificial Neural Networks: A Tutorial
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
Global Optimization for Neural Network Training
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
Simulating Artificial Neural Networks on Parallel Architectures
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
An Artificial Neural Network that Models Human Decision Making
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
Symbolic Representation of Neural Networks
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
IEEE Transactions on Computers
A feedforward bidirectional associative memory
IEEE Transactions on Neural Networks
An efficient learning algorithm for associative memories
IEEE Transactions on Neural Networks
On the complexity of training neural networks with continuous activation functions
IEEE Transactions on Neural Networks
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The aim of the current study is to assess the suitability of two Associative Memory (AM) models to character recognition problems The two AM models under scrutiny are a One-Shot AM (OSAM) and an Exponential Correlation AM (ECAM) We compare these AMs on the resultant features of their architectures, including recurrence, learning and the generation of domains of attraction We illustrate the impact of each of these features on the performance of each AM by varying the training set size, introducing noisy data and by globally transforming symbols Our results show that each system is suited to different character recognition problems.