The Fuzzy Logic System

Artificial intelligence or AI is in simple terms, the ability for a computer to learn on its own, without any input from the user, and it has the ability to think on its own and make its own decisions and is also able to understand human intelligence. This is done by using tools in many fields. These fields include, but are not limited to computer science, natural language and speech, machine learning and patter recognition, and computer vision (Luke Muehlhauser, 2014). This also includes psychology, cognitive science, neuroscience, economics, probability, and optimization and logic. These are only the beginnings of what is required to meet what is needed to even to begin to scratch the topic of

ABSTRACT- An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information [1]. Artificial Neural Networks (ANN) also called neuro-computing, or parallel distributed processing (PDP), provide an alternative approach to be applied to problems where the algorithmic and symbolic approaches are not well suited. The objective of the neural network is to transform the inputs into meaningful outputs. There are many researches which show that brain store information as pattern. Some of these patterns are very complicated and allows us to recognize from different angles. This paper gives a review of the artificial neural network and analyses the techniques in terms of performance.

In a narrow sense, fuzzy logic can be defined as a logical system, which is an expansion of multi-valued logic. Whereas in a wider sense it is almost similar with fuzzy sets theory. It is a method for computing based on "degrees of truth or fact" rather than the "true or false" (1 or 0). The idea of fuzzy logic was first proposed by Dr. Lotfi Zadeh of the University of California at Berkeley in the 1965 [65].

Knight, G., Roosa, M., Calderon-Tina, C., & Gonzales, N (2009), J. (2009). Methodological issues and research on Latino populations, In L. Sayas & J. Borrego, Jr. (Eds.) Handbook of U.S. Latino psychology: Development and community based perspectives (pp.45-62).

A power system is always in a state of disturbance that may lead to instability in the system. The consequences of a major power supply interruption can prove to be so disastrous, that every effort must be made to reduce the impact of such a disturbance. The process of determining the steadiness of the power system following any upset is known as security assessment. In particular, MW security assessment is a process to evaluate the security of the power system following a disturbance. It is done considering the loading conditions in respect of MW power flow on the lines. Each line has a capacity to carry MW power up to transmission line design limits beyond which the lines may trip due to overloading. In this paper MW security assessment has

For a perfectly planned power grid, the power generation of each region is matched with the power consumption within this region, so that the power lines will always carry a reasonable workload. If one power line fails to work, other parallel lines would have to carry extra power, which is bad for safety and may lead to fire. The power system operators are the ones

L.A. Zadeh, Fuzzy Sets [33] In 1965 the concept of fuzzy logic was first introduced by the Professor Lotfi A. Zadeh in the University of California, Berkeley [33]. Fuzzy logic is a powerful design system for implementing the artificial intelligence in the controller which provides simple and intuitive method for software engineers to implement logic in complex systems. This concept had been given in one amongst his research papers under the name Fuzzy logic or Fuzzy sets.

This system is divided in a knowledge base and inference rules. In the video, this system is used when it comes the control center. The employees are checking flights and regulating flows thanks to precious information given in their computer like the weather, the flow rate and a packaging monitoring.

Expert systems are also known as knowledge based systems. These systems rely on a basic set of rules for solving specific problems and are capable of learning. The laws are defined for the system by experts and then implemented using if-then rules. These systems basically imitate the expert’s thoughts in solving the problem. An example of this is a system that diagnosis medical conditions. The doctor would input the symptoms to the computer system and it would then ask more questions if need or give diagnoses. Other examples include banking systems for acceptance of loans, advanced calculators, and weather predictions.

Fuzzy system has been applied to various type of application or system. Fuzzy system is an approach of the computational intelligence use a collection of fuzzy functions and rules to reason about a data. Its use any Fuzzy Logic based system which use Fuzzy Logic as basic for knowledgeable representation using different forms of knowledge. The main function of fuzzy logic technology is it ability of propose an approximate solvent to an imprecisely formulated problem. Fuzzy Logic in other meaning can be said as a procedure paradigm that is based on how human was thinking. What can be said that fuzzy logic is closer to human reasoning than the classical logic. Where then it attempts to precisely formulate and exactly solve a mathematical

Sustainable development demands that the world must move towards a more sustainable energy future. CO2 Capture and Storage (CCS) has emerged as a new advance energy technology to facilitate this transition in addition to renewable energy. There are socio-technical uncertainties hampering the deployment of this energy system which is very complex to model. In this paper, our focus is on uncertainties regarding public acceptance. It is widely accepted in the literature that people’s decisions (especially those of lay stakeholders) towards CCS, are fuzzy, imprecise and ambiguous. The literature of the field, even reports that at one point the decision is stable, and at another it is unstable—clearly an inherent problem in the dialogue surrounding this topic and predicting these behaviors presents a tremendous mathematical challenge. In this paper, we have made the first attempt to explore the theoretical feasibility of fuzzy sets in modeling these socio-technological uncertainties. Using this hard science method, we have proposed a new viewpoint that improves, but also deviates from the existing predictive methodologies our colleagues have adopted in modeling and predicting stakeholders’ complex decision-making behaviors. Using eight key socio-technical parameters, we have proposed a design of a novel human intelligence model based on fuzzy logic, for predicting these complex human behaviors on CCS life cycle. The data used in modeling the proposed system

To go back to a previous example, my washing machine determines which type of cycle to use based on the weight of the clothing. A “small” load or a “medium” load is not rigidly defined, but the fuzzy logic allows the washing machine to approximate the type of cycle it should use. An amount of clothes on the cusp of both “small” and “medium” could be placed in either category, depending on other variables such as fabric mix and the amount of detergent (“Fuzzy logic”). In this specific example fuzzy logic and soft computing can lead to errors, such as a wash cycle erroneously stopping after the clothes become heavier due to

The purpose of this report is to give information on the subject known as Logical reasoning and its use in Computer Science and computers in general. A historical background behind logic and Logical reasoning is firstly given, followed by an overview of the modern subject and the types it’s divided into. The types are then explained. The overlap between the field of logic and that of computer science is also given an explanation. The report ends with a brief overview on the subject and its tie to computer science and computing.

Why, despite the long-standing critique levelled against the Logical Framework Approach (LFA), its use in the development project planning continues to spread? Discuss the pros and cons of the LFA and respond to critiques developed against it with reference from national and/or international development projects.

ABSTRACT: In this paper, we study some of the properties of multi intuitionistic fuzzy rw-continuous mappings and multi intuitionistic fuzzy rw-irresolute mappings in multi intuitionistic fuzzy topological spaces and prove some results on these.