Fuzzy reasoning

The fuzzy concepts in problem nnderstandings that emerge out of life complexity as it unfolds cannot be resolved at the same level of knowledge that one has when these problems appear. Only when our consciousness is expanded to a higher level than the tension fades away and the problems, being seen in a new light, are no longer problems. When problems dissolve, one can say that the fuzziness related to them has been transcended. 

The qualitative jump of consciousness to a higher level results in transcending the fuzziness. As far as consciousness is of a holistic characteristic of human, and perhaps not just of human, but also nature and not only a product of mind, but its growth and transformation are possible when the factors responsible for the integrity of all three inseparable constituents of human individuality, which are body. 

The fuzziness of understanding can be transcended when the consciousness resonance occurs. 

The consciousness resonance does not eliminate fuzziness, which is an eternal companion to any process of thinking and knowing. At the same time, when the consciousness resonance helps us to transcend the fuzziness related to a problem that dissolves, it opens space for new problems to emerge bringing with them new fuzziness to puzzle our thoughts and feelings. At any level of consciousness, there are infinite number of phenomena and processes challenging the swarm of our perceptions, of our beliefs and hopes, views and attitudes, aspirations and dreams. 

The term resonance has a clear meaning in physics—it is a process of initiating a vibratory response in a receiver that is attuned to an emitter, which is considered as a source of vibrations (causal facts) they can be periodic, aperiodic or chaotic. In the process of resonance, these vibrations fire sympathetic vibrations in the receiver (consequent), the magnitude of which is often greater than the magnitude of the vibratious generated by the emitter. 

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Equation (10.23) is referred to as the max-min inference process or max-min fuzzy reasoning. 

Rules and Confidence Factors. Facts that are known to the system are given a confidence level of either 0 (not true) or 100 (true). Facts that are not yet known to the system are assigned a confidence level of -1, and facts that are declared unknown are given a confidence level of -2. CORA was not structured to ask users for a degree of confidence in their answers. Each rule s conclusion is assigned either 0 if the rule fails or 100 if the rule succeeds. In that respect, CORA is not designed to deal with "fuzzy reasoning."... 

Baldwin, J. F., Fuzzy Logic and Fuzzy Reasoning, University of Bristol, Eng. Maths. Dept., Research Report EM/FS 14, September, 1978. 

This new method for quantifying chip breakabiiity was derived based on the fundamentals of fuzzy reasoning. It is assumed that the following three factors and the associated... 

Fuzzy reasoning involves two parts evaluating the rule antecedent (the IF part of the rule) and applying the result to the consequent (the THEN part of the rule). Like the rules in expert systems, a fuzzy rule can have multiple antecedents joined by fuzzy operators AND or OR, or multiple consequents joined by fuzzy operator AND. For example ... 

Develop a fuzzy reasoning approach to facilitate the quantification analysis of subjective conditional probability of the nodes under multiple parents. 

An assertion or belief is something understood or felt to be understood by the domain expert. There is no "truth" value or certainty check. It would be possible to indicate the certainty value of an assertion using a form of fuzzy reasoning or simply attaching a probability value to all assertions. 

Fujita, H., J. R. Imre, J. Fodor, M. Kurematsu, and J. Hakura, "Fuzzy Reasoning for Medical Diagnosis-Based Aggregation on Different Ontologies," IEEE 7th International Symposium on Applied Computational Intelligence and Informatics, pp. 137-146,2012. 

Fujita, H., "Fuzzy reasoning for medical diagnosis based on subjective attributes and objective attributes alignment," Proceedings of the 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, IFSA/NAFIPS, Art. no. 6608528, pp. 950-955,2013. 

Fuzzy multicriteria analysis is used for decision making in a network of procedures that describes a complete electrochemical finishing plant. The decision alternatives result by means of fault tree analysis and neuro-fuzzy reasoning the criteria are categorized as objective and subjective. The training of the technical staff is achieved in a cooperative environment by playing with what if scenarios based on real and simulated data. 

Other notations in Figure 7 are as follows. V t) is the result of fuzzy reasoning used to control the output power of liquid feed pump, while Ki and K2 represent gains of the input variables. 

Qualitative Analysis by Inductively Coupled Plasma Atomic Emission Spectrometry Based on a Fuzzy Reasoning Scheme. 

An, M., Huang, S. and Baker, C. (2007). Railway risk assessment-the fuzzy reasoning approach and fuzzy analytic hierarchy process approaches A case study of shunting at Waterloo depot. In Proceedings of the Institution of Mechanical Engineers, Part F Journal of Rail and Rapid Transit, 221(3), pp. 365-383. Bichou, K. (2008). Security and risk-based models in shipping and ports review and critical analysis. OECD/ITF Joint Transport Research Centre Discussion Paper. 

This paper suggests an evaluation of an airport security system based on a hierarchical model of fuzzy reasoning. This approach results from the fact that the tested system is heavily influenced by the human factor and other elements, which are not subject to unequivocal and precise description. It is impossible to detect the functional relationships between the various factors which influence the effectiveness of the security system and an evaluation of the security level. In such cases it is required to use expert opinions. As we have to deal with expert opinions, it is a known fact that very often they are formulated in a descriptive and an imprecise way. We must therefore view the decision making problem in context of uncertainty related to decision making (Dubois Prade 1992). All this locates the decision making problem in an area described by e.g. the theory of fuzzy sets or rough sets (Greco et al. 2001). 

This paper focuses on fuzzy reasoning. A similar approach, although related to the evaluation of an aircrew status, was presented in the work by (Skorupski Wiktorowski 2013). This paper is a followup to the work by (Skorupski Uchronski 2014), where the general structure of interaction model in the airport security model is discussed in detail. 

If we write down the reasoning scheme (3) in the form of a fuzzy reasoning system we will reach the following form... 

Table 1. Fuzzy reasoning rules for the TIP Evaluation local model. 

A fuzzy reasoning system implemented in the SciLab package and applied in Section 5 uses the above inference reasoning method specified in formulas (8-10). 

The subject of analysis in this section is the Device evaluation fuzzy model. Its diagram is presented in Figure 2. The output of the fuzzy reasoning system (y associated with this fuzzy model is dependent on four input variables Detectability(x ), TIP evaluation yf Number of detection lines (x ), Device age (x ). 

The output variable of the local fuzzy reasoning model for TIP evaluation is presented in Figure 6. This variable is at the same time the input variable for the device evaluation model. The example assumed fuzzy reasoning rules are shown in Table 1. 

The fuzzy reasoning system is completed by 301 fuzzy rules defined by expert practitioners some examples are shown in Table 2. 

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