This paper shows on developing agent based modelling for represent the performance of doing logic programming in Hopfield network by using a new activation function. The effects of the activation function on the performance of the neuro-symbolic integration are analyzed mathematically and compared with the existing method. Computer simulations are carried out to validate the effectiveness on the new activation function. The resuls obtained showed that the new activation function outperform the existing method in doing logic programming in Hopfield network. The models developed by agent based modelling also support this theory
The development of artificial neural network and logic programming plays an important part in neural...
Modeling higher order cognitive processes like human decision making come in three representational ...
Modeling higher order cognitive processes like human decision making come in three representational ...
Logic program and neural networks are two important aspects in artificial intelligence. This paper i...
Logic program and neural networks are two important aspects in artificial intelligence. This paper i...
Logic program and neural networks are two important perspectives in artificial intelligence. The maj...
We will develop agent based modelling (ABM) for doing logic programming and Reverse Analysis method ...
Artificial Neural Network (ANN) uses many activation functions to update the state on neuron. The re...
Neural network and logic integration is the latest trend in Artificial Intelligence. Neural Symbolic...
This paper presents a new approach to upgrade the performance of logic programming in Hopfield netwo...
Copyright © 2014 Saratha Sathasivam. This is an open access article distributed under the Creative C...
For higher-order programming, higher order network architecture is necessary as high order neural ne...
We discuss the purpose of neural-symbolic integration including its principles, mechanisms and appli...
We discuss the purpose of neural-symbolic integration including its principles, mechanisms and appli...
We discuss the purpose of neural-symbolic integration including its principles, mechanisms and appli...
The development of artificial neural network and logic programming plays an important part in neural...
Modeling higher order cognitive processes like human decision making come in three representational ...
Modeling higher order cognitive processes like human decision making come in three representational ...
Logic program and neural networks are two important aspects in artificial intelligence. This paper i...
Logic program and neural networks are two important aspects in artificial intelligence. This paper i...
Logic program and neural networks are two important perspectives in artificial intelligence. The maj...
We will develop agent based modelling (ABM) for doing logic programming and Reverse Analysis method ...
Artificial Neural Network (ANN) uses many activation functions to update the state on neuron. The re...
Neural network and logic integration is the latest trend in Artificial Intelligence. Neural Symbolic...
This paper presents a new approach to upgrade the performance of logic programming in Hopfield netwo...
Copyright © 2014 Saratha Sathasivam. This is an open access article distributed under the Creative C...
For higher-order programming, higher order network architecture is necessary as high order neural ne...
We discuss the purpose of neural-symbolic integration including its principles, mechanisms and appli...
We discuss the purpose of neural-symbolic integration including its principles, mechanisms and appli...
We discuss the purpose of neural-symbolic integration including its principles, mechanisms and appli...
The development of artificial neural network and logic programming plays an important part in neural...
Modeling higher order cognitive processes like human decision making come in three representational ...
Modeling higher order cognitive processes like human decision making come in three representational ...