Abstract
In this paper we present a theoretical model based on soft computing to distribute the time/cost among the industry/machine sensors or effectors based on the type of the application. One of the most unstudied significant work is to recognize which sensor in an industry for example has higher priority than others. This is important to know which sensor to be checked first and within time limits of the system response. The problem of such systems is their variant environmental situations. Based on these varied situations, the priority of the importance of each sensor might change from time to another. Due to this uncertainty and lack of some information, soft computing is considered to be one of the plausible solutions. The presented idea is based on initially training of the system and continuously exploiting the system experience of the degree of importance of the sensors. The proposed system has three main stages, the first stage is concerned with training the system to obtain the necessary system time to respond, the necessary time allocated to recognize which sensors to check (or which has higher priority), and the initial importance value for each sensor, which indicates the initial judgment about the sensor importance. The second stage is to use the system experience about the importance of the sensor using fuzzy logic to decide the final values of each sensor 's importance. Based on the output of the second stage and the output of the first stage, the system distributes the time/cost among the sensors (some sensors with lower priority might be neglected). The main idea of the proposed work is based on neurofuzzy.
Highlights
We introduce the soft computing and its applications
The main difference between this approach and the approach which we propose is that the system in [15] is based on neuroevolution and fitness function to decide the degree of the importance of the sensor, whereas in our proposed system as we shall see, the degree of the importance of a sensor is based on the system experience which uses the neurofuzzy to decide it
A theoretical model to solve the problem of distributing a slot of time to decide which sensors in industry or controller have more importance and effect than others in system response within time limits is developed
Summary
Soft computing (SC) is a term originally expressed by Lotfi Zadeh [1][2] to denote systems "exploit the tolerance for imprecision, uncertainty, and partial truth to achieve tractability, robustness, low solution cost, and better rapport with reality" [2]. In difficult real-world learning tasks such as controlling robots, playing games, or pursuing or evading an enemy, there are no direct targets that would specify correct actions for each situation. In such problems, optimal behavior must be learned by exploring different actions, and assigning credit for good decisions based on sparse reinforcement feedback. Some of the very common systems for applications is applying Neural networks, genetic algorithms, fuzzy systems, evolutionary computing or combination of them in the real time industry system. Games where players should do some action or otherwise destroyed by other player
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