scholarly journals Extreme Learning Machine Neural Networks for Multi-Agent System in Power Generation

2018 ◽  
Vol 7 (4.35) ◽  
pp. 347
Author(s):  
Chong Tak Yaw ◽  
Shen Yuong Wong ◽  
Keem Siah Yap ◽  
Chin Hooi Tan
Keyword(s):  
Power Generation ◽  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Satellite Image ◽  
Multi Agent System ◽  
Classification Problems ◽  
Agent System ◽  
Individual Agent ◽  
Multi Agent ◽  
Learning Machine

Extreme Learning Machine (ELM) is widely known as an effective learning algorithm than the conventional learning methods from the point of learning speed as well as generalization. The hidden neurons are optional in neuron alike whereas the weights are the criteria required to study the linking among the output layer as well as hidden layers. On the other hand, the ensemble model to integrate every independent prediction of several ELMs to produce a final output. This particular approach was included in a Multi-Agent System (MAS). By hybrid those two approached, a novel extreme learning machine based multi-agent systems (ELM-MAS) for handling classification problems is presented in this paper. It contains two layers of ELMs, i.e., individual agent layer and parent agent layer. Several activation functions using benchmark datasets and real-world applications, i.e., satellite image, image segmentation, fault diagnosis in power generation (including circulating water systems as well as GAST governor) were used to test the ELM-MAS developed. Our experimental results suggest that ELM-MAS is capable of achieving good accuracy rates relative to others approaches.

scholarly journals Extreme Learning Machine with Multi-Agent System for Regression

2019 ◽  
Vol 9 (2) ◽  
pp. 4149-4153
Keyword(s):  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Multi Agent System ◽  
Agent System ◽  
Individual Agent ◽  
Learning Speed ◽  
Benchmark Datasets ◽  
Multi Agent ◽  
Learning Machine ◽  
Hidden Neurons

From the point of learning speed as well as generalization, Extreme Learning Machine(ELM) is widely known as an effective learning algorithm than the conventional learning methods. Basically, hidden neurons are not required in neuron alike, instead, weight is the parameter that would need to learn about the link in between output and hidden layers. The creation of an output is to integrate each independent of several ELMs. The precise approach is included in a Multi-Agent System. The novelty of ELM-MAS (extreme learning machine based multi-agent system) is put forward in the paper for solving data regression problems. The ELMs consist of two layers which are the parent agent layer and individual agent layer. The effectiveness of the ELM-MAS model is proved with some activation functions employing benchmark datasets (abalone, strike and space-ga) and real world application (Nox emission). The outcomes indicate that the proposed model is capable to attain improved results than other approaches.

scholarly journals Multi-Agent System Based on the Extreme Learning Machine and Fuzzy Control for Intelligent Energy Management in Microgrid

2018 ◽  
Vol 29 (1) ◽  
pp. 877-893 ◽  
Author(s):  
Dounia El Bourakadi ◽  
Ali Yahyaouy ◽  
Jaouad Boumhidi
Keyword(s):  
Extreme Learning Machine ◽  
Electricity Market ◽  
Weather Data ◽  
Multi Agent System ◽  
Power Prediction ◽  
Agent System ◽  
On Demand ◽  
Multi Agent ◽  
Learning Machine ◽  
The Cost

Abstract Renewable energies constitute an alternative to fossil energies for several reasons. The microgrid can be assumed as the ideal way to integrate a renewable energy source in the production of electricity and give the consumer the opportunity to participate in the electricity market not just like a consumer but also like a producer. In this paper, we present a multi-agent system based on wind and photovoltaic power prediction using the extreme learning machine algorithm. This algorithm was tested on real weather data taken from the region of Tetouan City in Morocco. The process aimed to implement a microgrid located in Tetouan City and composed of different generation units (solar and wind energies were combined together to increase the efficiency of the system) and storage units (batteries were used to ensure the availability of power on demand as much as possible). In the proposed architecture, the microgrid can exchange electricity with the main grid; therefore, it can buy or sell electricity. Thus, the goal of our multi-agent system is to control the amount of power delivered or taken from the main grid in order to reduce the cost and maximize the benefit. To address uncertainties in the system, we use fuzzy logic control to manage the flow of energy, to ensure the availability of power on demand, and to make a reasonable decision about storing or selling electricity.

scholarly journals Multi-agent cooperation Q-learning algorithm based on constrained Markov Game

2020 ◽  
Vol 17 (2) ◽  
pp. 647-664
Author(s):  
Yangyang Ge ◽  
Fei Zhu ◽  
Wei Huang ◽  
Peiyao Zhao ◽  
Quan Liu
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Multi Agent System ◽  
Agent System ◽  
Action Function ◽  
Q Learning ◽  
State Action ◽  
Markov Game ◽  
Safety Constraints ◽  
Multi Agent

Multi-Agent system has broad application in real world, whose security performance, however, is barely considered. Reinforcement learning is one of the most important methods to resolve Multi-Agent problems. At present, certain progress has been made in applying Multi-Agent reinforcement learning to robot system, man-machine match, and automatic, etc. However, in the above area, an agent may fall into unsafe states where the agent may find it difficult to bypass obstacles, to receive information from other agents and so on. Ensuring the safety of Multi-Agent system is of great importance in the above areas where an agent may fall into dangerous states that are irreversible, causing great damage. To solve the safety problem, in this paper we introduce a Multi-Agent Cooperation Q-Learning Algorithm based on Constrained Markov Game. In this method, safety constraints are added to the set of actions, and each agent, when interacting with the environment to search for optimal values, should be restricted by the safety rules, so as to obtain an optimal policy that satisfies the security requirements. Since traditional Multi-Agent reinforcement learning algorithm is no more suitable for the proposed model in this paper, a new solution is introduced for calculating the global optimum state-action function that satisfies the safety constraints. We take advantage of the Lagrange multiplier method to determine the optimal action that can be performed in the current state based on the premise of linearizing constraint functions, under conditions that the state-action function and the constraint function are both differentiable, which not only improves the efficiency and accuracy of the algorithm, but also guarantees to obtain the global optimal solution. The experiments verify the effectiveness of the algorithm.

Reward-based epigenetic learning algorithm for a decentralised multi-agent system

2020 ◽  
Vol 8 (3) ◽  
pp. 201-224
Author(s):  
Faqihza Mukhlish ◽  
John Page ◽  
Michael Bain
Keyword(s):  
Swarm Robotics ◽  
Learning Algorithm ◽  
Future Research ◽  
Evolutionary Learning ◽  
Multi Agent System ◽  
Test Functions ◽  
Agent System ◽  
Content Type ◽  
Multi Agent ◽  
Future Research Directions

PurposeThis paper aims to propose a novel epigenetic learning (EpiLearn) algorithm, which is designed specifically for a decentralised multi-agent system such as swarm robotics.Design/methodology/approachFirst, this paper begins with overview of swarm robotics and the challenges in designing swarm behaviour automatically. This should indicate the direction of improvements required to enhance an automatic swarm design. Second, the evolutionary learning (EpiLearn) algorithm for a swarm system using an epigenetic layer is formulated and discussed. The algorithm is then tested through various test functions to investigate its performance. Finally, the results are discussed along with possible future research directions.FindingsThrough various test functions, the algorithm can solve non-local and many local minima problems. This article also shows that by using a reward system, the algorithm can handle the deceptive problem which often occurs in dynamic problems. Moreover, utilization of rewards from the environment in the form of a methylation process on the epigenetic layer improves the performance of traditional evolutionary algorithms applied to automatic swarm design. Finally, this article shows that a regeneration process that embeds an epigenetic layer in the inheritance process performs better than a traditional crossover operator in a swarm system.Originality/valueThis paper proposes a novel method for automatic swarm design by taking into account the importance of multi-agent settings and environmental characteristics surrounding the swarm. The novel evolutionary learning (EpiLearn) algorithm using an epigenetic layer gives the swarm the ability to perform co-evolution and co-learning.

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