A high-performance VLSI architecture for the histogram peak-climbing data clustering algorithm
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Robust fuzzy clustering neural network based on ε-insensitive loss function
Applied Soft Computing
| Hi-index | 0.00 |
A system design methodology for fuzzy clustering neural networks (FCNs) is presented. This methodology emphasizes coordination between FCN model definition, architectural description, and systolic implementation. Two mapping strategies both from FCN model to system architecture and from the given architecture to systolic arrays are described. The effectiveness of the methodology is illustrated by: 1) applying the design to an effective FCN model; 2) developing the corresponding parallel architecture with special feedforward and feedback paths; and 3) building the systolic array suitable for VLSI implementation