Issue 3 • Sep 1990
Cited by: Papers (11)
An associative neural network whose architecture is greatly influenced by biological data is described. The proposed neural network is significantly different in architecture and connectivity from previous models. Its emphasis is on high parallelism and modularity. The network connectivity is enriched by recurrent connections within the modules. Each module is, effectively, a Hopfield net. Connect... View full abstract»
Cited by: Papers (20)
The minimal number of times for using a pair for training to guarantee recall of that pair among a set of training pairs is derived for a bidirectional associative memory View full abstract»
Cited by: Papers (28) | Patents (1)
A perceptron learning algorithm may be viewed as a steepest-descent method whereby an instantaneous performance function is iteratively minimized. An appropriate performance function for the most widely used perceptron algorithm is described and it is shown that the update term of the algorithm is the gradient of this function. An example is given of the corresponding performance surface based on ... View full abstract»
Cited by: Papers (38)
A parallel algorithm for finding a near-maximum independent set in a circle graph is presented. An independent set in a graph is a set of vertices, no two of which are adjacent. A maximum independent set is an independent set whose cardinality is the largest among all independent sets of a graph. The algorithm is modified for predicting the secondary structure in ribonucleic acids (RNA). The propo... View full abstract»
Aims & Scope
IEEE Transactions on Neural Networks is devoted to the science and technology of neural networks, which disclose significant technical knowledge, exploratory developments, and applications of neural networks from biology to software to hardware.
This Transactions ceased production in 2011. The current retitled publication is IEEE Transactions on Neural Networks and Learning Systems.