Hierarchical Intelligent Control Method for Mineral Particle Size Based on Machine Learning

Hierarchical extreme learning machine based reinforcement learning for goal localization

The objective of goal localization is to find the location of goals in noisy environments. Simple actions are performed to move the agent towards the goal. The goal detector should be capable of minimizing the error between the predicted locations and the true ones. Few regions need to be processed by the agent to reduce the computational effort and increase the speed of convergence. In this pa...

Machine Learning for Intelligent Agents

Machine Learning for Intelligent Systems

Recent research in machine learning has focused on supervised induction for simple classification and reinforcement learning for simple reactive behaviors. In the process, the field has become disconnected from AI’s original goal of creating complete intelligent agents. In this paper, I review recent work on machine learning for planning, language, vision, and other topics that runs counter to ...

Design of Intelligent Control Systems Based on Hierarchical Stochastic Automata

In this age of modern era, the use of internet must be maximized. Yeah, internet will help us very much not only for important thing but also for daily activities. Many people now, from any level can use internet. The sources of internet connection can also be enjoyed in many places. As one of the benefits is to get the on-line design of intelligent control systems based on hierarchical stochas...

Emotional Learning Based Intelligent Controller for MIMO Peripheral Milling Process

During the milling process, one of the most important factors in reducing tool life expectancy and quality of workpiece is the chattering phenomenon due to self-excitation. The milling process is considered as a MIMO strongly coupled nonlinear plant with time delay terms in cutting forces. We stabilize the plant using two independent Emotional Learning-based Intelligent Controller (ELIC) in par...