Despite the well-known performances and the theoretical power of neural networks, learning and generalizing are sometimes very difficult. In this article, we investigate how short ...
Abstract The Little-Hopfield neural network programmed with Horn clauses is studied. We argue that the energy landscape of the system, corresponding to the inconsistency function f...
Saratha Sathasivam, Wan Ahmad Tajuddin Wan Abdulla...
This paper presents our Recurrent Control Neural Network (RCNN), which is a model-based approach for a data-efficient modelling and control of reinforcement learning problems in di...
- This paper deals with the application of a well-known data mining technique, multi-layer back-propagation neural network, for forecasting of an avalanche in Himalayan region. Met...
Rashpal Kaur, Mahesh Bansal, Atul Parti, V. Rihani
The paper examines characteristics of interactive learning between human tutors and a robot having a dynamic neural network model which is inspired by human parietal cortex functio...
Jun Tani, Ryunosuke Nishimoto, Jun Namikawa, Masat...