Abstract
Acceleration technique for neuro symbolic integration
Highlights
The discrete Hopfield neural network is a feedback network which operates as an efficient associative memory, and it store certain memories in a manner rather similar to the brain
Fuzzy Hopfield neural network clustering technique assimilated with modifying activation function are proposed to solve combinatorial optimization problems
We will analyse the usage of fuzzy Hopfield neural network clustering method with modifying activation function in accelerating the computation of doing logic programming in Hopfield network
Summary
The discrete Hopfield neural network is a feedback network which operates as an efficient associative memory, and it store certain memories in a manner rather similar to the brain. Wan Abdullah [1, 2] introduced a technique for doing logic programming on a Hopfield network. Fuzzy Hopfield neural network clustering technique assimilated with modifying activation function are proposed to solve combinatorial optimization problems. Fuzzy Hopfield neural network is a numerical procedure to minimize energy function to find membership grade. Combinatorial optimization consists of searching for the combination of choices from a discrete set which gives an optimal value for corresponding cost function. In fuzzy Hopfield neural network, it imposed a fuzzy c-means clustering algorithm to activate the neuron states each time in Hopfield neural network. We will analyse the usage of fuzzy Hopfield neural network clustering method with modifying activation function in accelerating the computation of doing logic programming in Hopfield network
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
