Wyniki wyszukiwania - BazTech

1

A comparative analysis of artificial intelligence-based methods for fault diagnosis of mechanical systems

Moghaddam Reihaneh Kardehi, Yazdan Navid Moshtaghi

Mechanics and Mechanical Engineering

|

2019

|

Vol. 23, nr 1

113--124

EN

The present research studied fault diagnosis of composite sheets using vibration signal processing and artificial intelligence (AI)-based methods. To this end, vibration signals were collected from sound and faulty composite plates. Using different time-frequency signal analysis and processing methods, a number of features were extracted from these signals and the most effective features containing further information on these composite plateswere provided as input to different classification systems. The output of these classification systems reveals the faults in composite plates. The different types of classification systems used in this research were the support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), k-nearest neighbor (k-NN), artificial neural networks (ANNs), Extended Classifier System (XCS) algorithm, and the proposed improved XCS algorithm. The research results were reflective of the superiority of ANFIS in terms of precision,while this method had the highest process duration with an equal number of iterations. The precision of the proposed improved XCS methodwas lower than that of ANFIS, but the duration of the processwas shorter than the ANFIS method with an equal number of iterations.

2

A Recursive Classifier System for Partially Observable Environments

Hamzeh A., Hashemi S., Sami A., Rahmani A.

Fundamenta Informaticae

|

2009

|

Vol. 97, nr 1/2

15-40

EN

Previously we introduced Parallel Specialized XCS (PSXCS), a distributed-architecture classifier system that detects aliased environmental states and assigns their handling to created subordinate XCS classifier systems. PSXCS uses a history-window approach, but with novel efficiency since the subordinateXCSs, which employ the windows, are only spawned for parts of the state space that are actually aliased. However, because the window lengths are finite and set manually, PSXCS may fail to be optimal in difficult test mazes. This paper introduces Recursive PSXCS (RPSXCS) that automatically spawns windows wherever more history is required. Experimental results show that RPSXCS is both more powerful and learns faster than PSXCS. The present research suggests new potential for history approaches to partially observable environments.

3

A New Architecture for Learning Classifier Systems to Solve POMDP Problems

Hamzeh A., Rahmani A.

Fundamenta Informaticae

|

2008

|

Vol. 84, nr 3-4

329-351

EN

Reinforcement Learning is a learning paradigm that helps the agent to learn to act optimally in an unknown environment through trial and error. An RL-based agent senses its environmental state, proposes an action, and applies it to the environment. Then a reinforcement signal, called the reward, is sent back from the environment to the agent. The agent is expected to learn how to maximize overall environmental reward through its internal mechanisms. One of the most challenging issues in the RL area arises as a result of the sensory ability of the agent, when it is not able to sense its current environmental state completely. These environments are called partially observable environments. In these environments, the agent may fail to distinguish the actual environmental state and so may fail to propose the optimal action in particular environmental states. So an extended mechanism must be added to the architecture of the agent to enable it to perform optimally in these environments. On the other hand, one of the most-used approaches to reinforcement learning is the evolutionary learning approach and one of the most-used techniques in this family is learning classifier systems. Learning classifier systems try to evolve state-action-reward mappings to model their current environment through trial and error. In this paper we propose a new architecture for learning classifier systems that is able to perform optimally in partially observable environments. This new architecture uses a novel method to detect aliased states in the environment and disambiguates them through multiple instances of classifier systems that interact with the environment in parallel. This model is applied to some well-known benchmark problems and is compared with some of the best classifier systems proposed for these environments. Our results and detailed discussion show that our approach is one of the best techniques among other learning classifier systems in partially observable environments.