Fuzzy Logic Model Of Perception Research Articles

Cognitive diagnostic assessment based on knowledge structure

The purpose of this study is to provide an integrated method of fuzzy theory basis for individualized concept structure analysis. In order to insight the misconception of learning basic mathematics and progress teaching. This method integrates Fuzzy Logic Model of Perception (FLMP) and Interpretive Structural Modelling (ISM). The combined algorithm could analyze individualized concepts structure based on the comparisons with concept structure of expert. In this paper, some well-known knowledge structure assessment methods will be discussed. For item connection, Bart et al ordering theory and Takeya’s item relational structure provided ordering coefficient to construct item relationships and hierarchies. For concepts or skills connection, Warfield’s ISM and Lin et al Concept Advanced Interpretive Structural Modelling (CAISM) provided to construct graphic relationship among elements and display the individualized concept hierarchy structure by numeric and picture. Samples contain 427 which come from Min-Hwei Junior College. Subjects were analyzed by CAISM. It shows the traditional assessment is not the only criteria; it must be combined with other assessment tools. The result shows that CAISM gives meaningful learning and lacks of learners.

Management of Basic Mathematic Concepts for Cognition Diagnosis

Knowledge Management of Mathematics Concepts was essential in educational environment. The purpose of this study is to provide an integrated method of fuzzy theory basis for individualized concept structure analysis. This method integrates Fuzzy Logic Model of Perception (FLMP) and Interpretive Structural Modeling (ISM). The combined algorithm could analyze individualized concepts structure based on the comparisons with concept structure of expert. The empirical data is Basic Mathematic test of Junior College students. The object of concept advanced interpretive structural modeling (CAISM) is to provide individualized hierarchy structure of knowledge of examinees based on response patterns of tests. It shows that knowledge structures will be feasible for remedial instruction and this procedure will also useful for cognition diagnosis.

Construct Concept Structure for Abstract Algebra

Building relationship between conceptual knowledge and the procedures of mathematics contributes to long-term memory of procedures and to their effective use. So we know that symbols could enhance concept and procedures apply concepts to solve problem efficiently. The purpose of this study is to provide an integrated method of fuzzy theory basis for individualized concept structure analysis. This method integrates Fuzzy Logic Model of Perception (FLMP) and Interpretive Structural Modeling (ISM). The combined algorithm could analyze individualized concepts structure based on the comparisons with concept structure of expert.

Management of Abstract Algebra Concepts Based on Knowledge Structure

Knowledge Management of Mathematics Concepts was essential in educational environment. The purpose of this study is to provide an integrated method of fuzzy theory basis for individualized concept structure analysis. This method integrates Fuzzy Logic Model of Perception (FLMP) and Interpretive Structural Modeling (ISM). The combined algorithm could analyze individualized concepts structure based on the comparisons with concept structure of expert. Fuzzy clustering algorithms are based on Euclidean distance function, which can only be used to detect spherical structural clusters. A Fuzzy C-Means algorithm based on Mahalanobis distance (FCM-M) was proposed to improve those limitations of GG and GK algorithms, but it is not stable enough when some of its covariance matrices are not equal. A new improved Fuzzy C-Means algorithm based on a Normalized Mahalanobis distance (FCM-NM) is proposed. Use the best performance of clustering Algorithm FCM-NM in data analysis and interpretation. Each cluster of data can easily describe features of knowledge structures. Manage the knowledge structures of Mathematics Concepts to construct the model of features in the pattern recognition completely. This procedure will also useful for cognition diagnosis. To sum up, this integrated algorithm could improve the assessment methodology of cognition diagnosis and manage the knowledge structures of Mathematics Concepts easily.

Construct Concept Structure for Fundamental Mathematics

Currently, cognitive psychologists and mathematics educators are looking again at conceptual and procedural knowledge in mathematics learning. Building relationship between conceptual knowledge and the procedures of mathematics contributes to long-term memory of procedures and to their effective use. So we know that symbols could enhance concept and procedures apply concepts to solve problem efficiently. Sketch the graph of exponential function and logarithmic function. Find the inverse exponential function, logarithmic function and so on. The lack of other concrete example in general exponential and logarithmic function prevented the development of application about general exponential and logarithm. Numerical fundamental mathematic problems are provided with complicated number frequently. The lack of other concrete example in general exponential and logarithmic function prevented the development of application about general exponential and logarithm. Numerical fundamental mathematic problems are provided with complicated number frequently The conceptual knowledge, not mechanical algorithms, need more study and thought to identify. But there is a limitation for students use some materials to clarify complicated mathematic concepts perfectly. In order to insight the misconception of learning fundamental mathematics and progress teaching. The purpose of this study is to provide an integrated method of fuzzy theory basis for individualized concept structure analysis. This method integrates Fuzzy Logic Model of Perception (FLMP) and Interpretive Structural Modeling (ISM). The combined algorithm could analyze individualized concepts structure based on the comparisons with concept structure of expert. Applying the method of the cluster of fuzzy c-mean, we could distinguish characteristics of six groups. We analysis the whole data and discuss the relationship between knowledge structures of the sample. The result and discoveries from the research can offer pupils' misconception of learning fundamental mathematics with reference of diagnosis.

Construct Concept Structure for Linear Algebra Based on Cognition Diagnosis and Clustering with Mahalanobis Distances

Euclidean distance function based fuzzy clustering algorithms can only be used to detect spherical structural clusters. The purpose of this study is improved Fuzzy C-Means algorithm based on Mahalanobis distance to identify concept structure for Linear Algebra. In addition, Concept structure analysis (CSA) could provide individualized knowledge structure. CSA algorithm is the major methodology and it is based on fuzzy logic model of perception (FLMP) and interpretive structural modeling (ISM). CSA could display individualized knowledge structure and clearly represent hierarchies and linkage among concepts for each examinee. Each cluster of data can easily describe features of knowledge structures. The results show that there are five clusters and each cluster has its own cognitive characteristics. In this study, the author provide the empirical data for concepts of linear algebra from university students. To sum up, the methodology can improve knowledge management in classroom more feasible. Finally, the result shows that Algorithm based on Mahalanobis distance has better performance than Fuzzy C-Means algorithm.

Multinomial Processing Tree Models of Factorial Categorization

A general processing tree (GPT) model family (e.g., X. Hu & W. H. Batchelder, 1994,Psychometrika59, 21–47) is developed for measuring latent cognitive processes that may occur when subjects classify factorially defined stimuli. The GPT family for a two-factor, four-category design was shown to generalize Massaro and colleagues' fuzzy logic model of perception (FLMP) for this design. In fact, members of the family nest the GPT version of FLMP. The model family is based on the idea that the manifest categorical response depends on the joint outcomes of two dichotomous, latent processes. Closed-form expressions for maximum likelihood parameter estimates as well as their asymptotic variances are provided for all but one member of the model family. Members of the GPT family relax FLMP's processing assumptions in various ways, so these models can provide insight into the nature of processing in cases where FLMP is found to fail. Members of the model family were fitted to data from a speech perception experiment, and it was found that FLMP failed to fit the data, but two other models in the family provided an excellent account of the data. Finally, it is shown how to generalize the approach to different numbers of factors and categories

Multinomial models of speech perception

It is often the case that two different acoustic cues can influence perception of both of two adjacent phonemes within a syllable. In such cases, it is of interest to determine whether the cues are evaluated independently; that is, does a listener’s evaluation of one cue influence evaluation of the other cue? Another, related question is whether, as some researchers claim, phoneme decisions are dependent in the sense that, when cued by the same information, the decisions compete for the information. This study modeled data from a two factor, four category experiment in which F1 offset frequency and vowel duration cue both voicing and vowel identity in CVC syllables. A family of multinomial processing tree models [e.g., D. M. Riefer and W. H. Batchelder, Psychol. Rev. 95, 318–339 (1988)], of which the fuzzy logic model of perception for the two factor, four category design is a special case, was developed and tested to explore the independence issues. This modeling approach is highly flexible and allows one to test a wide range of hypotheses regarding speech cue processing.

A measurement-theoretic analysis of the fuzzy logic model of perception.

The fuzzy logic model of perception (FLMP) is analyzed from a measurement-theoretic perspective. FLMP has an impressive history of fitting factorial data, suggesting that its probabilistic form is valid. The authors raise questions about the underlying processing assumptions of FLMP. Although FLMP parameters are interpreted as fuzzy logic truth values, the authors demonstrate that for several factorial designs widely used in choice experiments, most desirable fuzzy truth value properties fail to hold under permissible rescalings, suggesting that the fuzzy logic interpretation may be unwarranted. The authors show that FLMP's choice rule is equivalent to a version of G. Rasch's (1960) item response theory model, and the nature of FLMP measurement scales is transparent when stated in this form. Statistical inference theory exists for the Rasch model and its equivalent forms. In fact, FLMP can be reparameterized as a simple 2-category logit model, thereby facilitating interpretation of its measurement scales and allowing access to commercially available software for performing statistical inference.

A measurement-theoretic analysis of the fuzzy logic model of perception.

A measurement-theoretic analysis of the fuzzy logic model of perception.