The bifurcating neuron network 1
Neural Networks
Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications
Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications
Complex-Valued Neural Networks: Theories and Applications (Series on Innovative Intelligence, 5)
Complex-Valued Neural Networks: Theories and Applications (Series on Innovative Intelligence, 5)
Implicative Fuzzy Associative Memories
IEEE Transactions on Fuzzy Systems
Complex-valued multistate neural associative memory
IEEE Transactions on Neural Networks
Neural associative memory storing gray-coded gray-scale images
IEEE Transactions on Neural Networks
A new design method for the complex-valued multistate Hopfield associative memory
IEEE Transactions on Neural Networks
Face recognition by applying wavelet subband representation and kernel associative memory
IEEE Transactions on Neural Networks
Associative memory design for 256 gray-level images using a multilayer neural network
IEEE Transactions on Neural Networks
Gray-scale morphological associative memories
IEEE Transactions on Neural Networks
Improvements of Complex-Valued Hopfield Associative Memory by Using Generalized Projection Rules
IEEE Transactions on Neural Networks
A Weighted Voting Model of Associative Memory
IEEE Transactions on Neural Networks
Analysis and synthesis of a class of discrete-time neural networks with multilevel threshold neurons
IEEE Transactions on Neural Networks
Permutation-based finite implicative fuzzy associative memories
Information Sciences: an International Journal
| Hi-index | 0.00 |
A widely used complex-valued activation function for complex-valued multistate Hopfield networks is revealed to be essentially based on a multilevel step function. By replacing the multilevel step function with other multilevel characteristics, we present two alternative complex-valued activation functions. One is based on a multilevel sigmoid function, while the other on a characteristic of a multistate bifurcating neuron. Numerical experiments show that both modifications to the complex-valued activation function bring about improvements in network performance for a multistate associative memory. The advantage of the proposed networks over the complex-valued Hopfield networks with the multilevel step function is more outstanding when a complex-valued neuron represents a larger number of multivalued states. Further, the performance of the proposed networks in reconstructing noisy 256 gray-level images is demonstrated in comparison with other recent associative memories to clarify their advantages and disadvantages.