Fuzzy sets, uncertainty, and information by George J. Klir, , Prentice Hall edition, in English. Publication Stages. Accepted Manuscript - Manuscripts that have been selected for publication. They have not been typeset and the text change before final . Some people be giggling when taking a look at you reviewing Fuzzy Sets, Uncertainty And Information, By George J. Klir, Tina A. Folger in.

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Intermediate representation Issues High level, medium level, low level intermediate languages MIR, HIR, LIR ICAN for Intermediate code Module 2 Run-time support Register usage local stack frame run-time stack Code sharing positionindependent code Symbolic and polymorphic language support - Optimization Early optimization Constant folding scalar replacement of aggregates Simplification value numbering constant propagation redundancy elimination loop optimization. Procedure optimization in-line expansion leaf routine optimization and shrink wrapping Module 3 Register allocation and assignment graph coloring control flow and low level optimizations Inter-procedural analysis and optimization call graph data flow analysis constant propagation alias analysis register allocation global References: Optimization for memory hierarchy. Steven S. Sivarama P. Alfred Aho, V.

The new generation- pulsed neuron model- Integrate and fire neurons- conductance based models. References: 1. Module 2: Genetic technology Steady state algorithm - fitness scaling - inversion. Genetic programming - Genetic Algorithm in problem solving Module 3: Genetic Algorithm in engineering and optimization Genetic Algorithm in engineering and optimization-natural evolution simulated annealing and Tabu search. Genetic Algorithm in scientific models and theoretical foundations computer implementation - low level operator and knowledge based techniques in Genetic Algorithm.

Module 4: Application Applications of Genetic based machine learning-Genetic Algorithm and parallel processors, constraint optimization, uses of GA in solving NP hard problems, multilevel optimization, real life problem. Golberg, Genetic algorithms in search, optimization and machine learning, Addition-Wesley Rajasekaran G. Nilsson, Artificial Intelligence- A new synthesis, Original edition Tutorial sessions: Latest research papers in GA.

Nils J. Fuzzy relation equation. Module 2: Fuzzy Preliminaries Expert Knowledge- Rules Antecedent and Consequents Forward and Backward Chaining Program Modularization and Blackboard systems Handling uncertainties in an expert system Fuzzification and defuzzification - Fuzzy Sets and Fuzzy Numbers- Algebra of Fuzzy Sets T norms and T conorms Approximate Reasoning Hedges Fuzzy Arithmetic extension principle alpha cut and interval arithmetic comparing between fuzzy numbers Module 3: Fuzzy Expert System Inference in Fuzzy Expert System - Types of fuzzy Inference nature of inference in a fuzzy expert system monotonic, non-monotonic, downward monotonic inference test of procedures modification of existing data by rule consequent instructions selection of reasoning type and grades of membership discrete fuzzy sets -invalidation of data : non-monotonic reasoning modeling the entire rule space conventional method data mining and combs method reducing number of required rules - running fuzzy expert systems.

Module 4: Running and Debugging Expert System Debugging tools Isolating Bugs data Acquisition from User Vs Automatic data Acquisition ways of solving one tree search problem Expert knowledge in Rules expert knowledge in database other applications of sequential rule firing rules that are referable - runaway programs and recursion Programs that learn from experience - Learning by adding rules Learning by adding facts general way of creating new rules and data descriptors detection of artifacts in input data stream data smoothing types of rules suitable for real time work memory management 35 References: 1.

George J. Klir, Tina A.

Module 2: Unsupervised Classification Clustering for unsupervised learning and classification - Clustering concept - C-means algorithm Hierarchical clustering procedures - Graph theoretic approach to pattern clustering - Validity of clustering solutions. In this paper, we overview different ways to fully store the expert information about imprecise properties.

We show that in the simplest case, when the only source of imprecision is disagreement between different experts, a natural way to store all the expert information is to use random sets; we also show how fuzzy sets naturally appear in such random-set representation.

Keywords: random sets, fuzzy sets, imprecise properties 1. Introduction Need to describe properties in computer-understandable terms In the modern world, we use computers in many important activities — to help us make decisions, to help us control different systems, etc.

To make computers as helpful as possible, it is desirable to make them understand and use — as much as possible — our knowledge about different objects.

How do we describe objects? To describe an object, usually: we list the properties that this object satisfies, and we describe numerical values of different quantities characterizing this property. For example, we can describe a person as blond property , tall property , with blue eyes property , and weighing 80 kg numerical value.

Thus, to make computers understand our knowledge, we must describe properties and numerical values in computer-understandable terms. It is easy to represent numerical values: computers were originally designed to represent and process numbers.

So, the remaining challenge is to represent properties. In the computer, all the information is represented as 0s and 1s, so this is also a very computer-friendly representation.

The problem with the 0—1 set representation is that this representation is only possible for precise well-defined properties; e. For such properties: once a person gets all needed information about the object, this person can uniquely decide whether this object satisfies the given property or not; and different people make the same decision about this property, i.

For example, to most people, someone 2 m high is clearly tall, while someone 1. It could also be that different people have different opinions about who is tall and who is not tall. For example, to most people, 1. But the aforementioned methods are somehow not that effective in some cases [ 71 , 75 ]. Lately, another uncertainty measure named Deng entropy [ 75 ] is proposed in Dempster-Shafer framework.

Although Deng entropy has been successfully applied in some real applications [ 36 — 38 , 80 , 81 ], it does not take into consideration the scale of the FOD, which means a loss of available information while doing information processing.

To make matters worse, the information loss will lead to failure for uncertainty measure in some cases [ 71 ]. The same shortage also exists in the confusion measure [ 66 ], the dissonance measure [ 67 ], the weighted Hartley entropy [ 68 ], the discord measure [ 69 ], and the strife measure [ 70 ].

To address this issue, an improved belief entropy based on Deng entropy is proposed in this paper. The proposed belief entropy can improve the performance of Deng entropy by considering the scale of the FOD and the relative scale of a focal element with respect to FOD. What is more, the proposed method keeps all the merits of Deng entropy; thus it can degenerate to Shannon entropy in the sense of the probability consistency.

In order to verify the validity of the improved belief entropy, a decision-making approach in target identification is designed based on the new belief entropy. The rest of this paper is organized as follows. In Section 2 , the preliminaries on Dempster-Shafer evidence theory, Shannon entropy, and some uncertainty measures in Dempster-Shafer framework are briefly introduced. In Section 3 , the improved belief entropy is proposed; some behaviors of the proposed belief entropy are discussed with the numerical examples.

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