Designing boosting ensemble of relational fuzzy systems

A method frequently used in classification systems for improving classification accuracy is to combine outputs of several classifiers. Among various types of classifiers, fuzzy ones are tempting because of using intelligible fuzzy if-then rules. In the paper we build an AdaBoost ensemble of relational neuro-fuzzy classifiers. Relational fuzzy systems bond input and output fuzzy linguistic values by a binary relation; thus, fuzzy rules have additional, comparing to traditional fuzzy systems, weights - elements of a fuzzy relation matrix. Thanks to this the system is better adjustable to data during learning. In the paper an ensemble of relational fuzzy systems is proposed. The problem is that such an ensemble contains separate rule bases which cannot be directly merged. As systems are separate, we cannot treat fuzzy rules coming from different systems as rules from the same (single) system. In the paper, the problem is addressed by a novel design of fuzzy systems constituting the ensemble, resulting in normalization of individual rule bases during learning. The method described in the paper is tested on several known benchmarks and compared with other machine learning solutions from the literature.     

Risk-Based Evaluation of Turkish Agricultural Strategies using Fuzzy AHP and Fuzzy ANP

In this article, we propose a model for selection of an agricultural strategy for Turkey by using fuzzy analytical hierarchy process (AHP) and analytical network process (ANP) based on linguistic terms. Fuzzy AHP-based methodology will be discussed to tackle the different decision criteria such as risk factors, ecological structure, socioeconomic structure, and technological structure involved in the selection of an agricultural strategy for Turkey and ANP will represent an effective tool for providing a suitable solution for managers and government administrators of this case. The linguistic levels of comparisons produced by the experts for each comparison are tapped in the form of triangular fuzzy numbers in order to construct fuzzy pairwise comparison matrices. The implementation of the system is demonstrated by a problem having four stages of hierarchy that contains 4 criteria and 15 sub-criteria.     

A fuzzy-genetic model for estimating forces from electromyographical activity of antagonistic muscles due to planar lower arm movements: the effect of nonlinear muscle properties

The aim of this paper is to create a model for mapping the surface electromyogram (EMG) signals to the force that generated by human arm muscles. Because the parameters of each person's muscle are individual, the model of the muscle must have two characteristics: (1) The model must be adjustable for each subject. (2) The relationship between the input and output of model must be affected by the force-length and the force-velocity behaviors are proven through Hill's experiments. Hill's model is a kinematic mechanistic model with three elements, i.e. one contractile component and two nonlinear spring elements.In this research, fuzzy systems are applied to improve the muscle model. The advantages of using fuzzy system are as follows: they are robust to noise, they prove an adjustable nonlinear mapping, and are able to model the uncertainties of the muscle.Three fuzzy coefficients have been added to the relationships of force-length (active and passive) and force-velocity existing in Hill's model. Then, a genetic algorithm (GA) has been used as a biological search method that can adjust the parameters of the model in order to achieve the optimal possible fit.Finally, the accuracy of the fuzzy genetic implementation Hill-based muscle model (FGIHM) is invested as following: the FGIHM results have 12.4% RMS error (in worse case) in comparison to the experimental data recorded from three healthy male subjects. Moreover, the FGIHM active force-length relationship which is the key characteristics of muscles has been compared to virtual muscle (VM) and Zajac muscle model. The sensitivity of the FGIHM has been evaluated by adding a white noise with zero mean to the input and FGIHM has proved to have lower sensitivity to input noise than the traditional Hill's muscle model.     

An Integrated MCDM Model for Conveyor Equipment Evaluation and Selection in an FMC Based on a Fuzzy AHP and Fuzzy ARAS in the Presence of Vagueness

The conveyor system plays a vital role in improving the performance of flexible manufacturing cells (FMCs). The conveyor selection problem involves the evaluation of a set of potential alternatives based on qualitative and quantitative criteria. This paper presents an integrated multi-criteria decision making (MCDM) model of a fuzzy AHP (analytic hierarchy process) and fuzzy ARAS (additive ratio assessment) for conveyor evaluation and selection. In this model, linguistic terms represented as triangular fuzzy numbers are used to quantify experts’ uncertain assessments of alternatives with respect to the criteria. The fuzzy set is then integrated into the AHP to determine the weights of the criteria. Finally, a fuzzy ARAS is used to calculate the weights of the alternatives. To demonstrate the effectiveness of the proposed model, a case study is performed of a practical example, and the results obtained demonstrate practical potential for the implementation of FMCs.     

Neuro-fuzzy prediction of uricase production

Recent biotechnology requires implementation of new modelling methods based on knowledge principles and learning structures, comprised in fuzzy knowledge-based systems (FKBS), neural networks (NN) and different hybrid methods. The intelligent modelling approaches solve sufficiently a very important problem - processing of scarce, uncertainty and incomplete numerical and linguistic information about multivariate non-linear and non-stationary systems as well as biotechnological processes. The paper deals with prediction of an enzyme oxidizing uric acid to alantoin - the uricase, produced by Candida utilis 90-12 employing neuro-fuzzy knowledge-based approach. The implemented predictive technique exploits the fact that the fuzzy model can be seen as a network structure, similar to artificial NN, which on computational level assure a high model accuracy. The predictors implemented are four different by nature variables. The developed predictive model shows that best predictors of uricase production are biomass and limiting substrate concentrations.     

Minimizing Environmental Risks Using Fuzzy TOPSIS: Location Selection for the ITU Faculty of Management

ABSTRACT

Environmental risk is a measure of potential threats to the environment and combines the probability that events will cause or lead to degradation of the environment and the severity of that degradation. Evaluation of environmental risk factors requires linguistic terms to be used rather than exact numerical assignments. The fuzzy set theory provides a useful tool for converting linguistic terms into numerical evaluations. In this article, we will try to select the best location for the Faculty of Management of the Istanbul Technical University taking care of the predetermined environmental criteria by using fuzzy TOPSIS. The decision matrix is composed of linguistic evaluations for the alternatives with respect to the environmental risk criteria. The Macka Campus area has been selected after the multicriteria evaluation. A sensitivity analysis is also realized to see if the minor changes in criteria weights cause any change in the ranking of alternatives. Unless the importance of the criteria rock or soil structure, remoteness to health facilities, transportation availability, and transportation costs is changed to be from low to extremely low importance, the Macka Campus is still the selected alternative.     

A genetic-algorithm-based approach to optimization of bioprocesses described by fuzzy rules

A new approach to optimization of bioprocesses described by fuzzy rules is introduced in the paper. It is based on genetic algorithms (GA) and allows to determine optimal values or profiles of control variables and to optimize fuzzy rules (parameters of membership functions). The process can be described by linguistic variables and fuzzy rules. An algorithm and related software was developed. The approach was applied to an industrial antibiotic fermentation. The optimal profile of a physical variable of the preculture was determined which leads to an increasing output product concentration in the main culture of about 5%.     

Historical processes and biological structure of the populations. Example from the Island of Korcula

The importance of geographical distance as a determining factor of the population's biological and socio-cultural structure was studied on the island of Korcula, Yugoslavia. Migrational kinship coefficients, biological and linguistic distances were analysed in respect to isolation by distance, using Malecot's model. The fit of the model is highly significant for migrational kinship, linguistic, anthropometrical and physiological distances, while for morphometrical distances of metacarpal bones and dermatoglyphic distances, it is not adequate. The major conclusions reached through this analysis are in concordance with the known historical events in the entire region.     

On the Application of Fuzzy Decision Theory in Chemotherapy

1IntreductionTheliteratUreonmulti-Criteriondecisionmaking(MCDM)problemshas~tremendouslyintherecentpast.TwomajorareashaveevolvedwhiChbothconcentrateondecisionmakingwithseveralcriteria:multiobjectivedecisionmaking(MODM)andmulti-attributedecisionmaking(MADM).TheformerconcentratesoncontinuousdecisionspaceandthelatterfocusesonproblemswithdiscreteSPace.FuzzysettheoryhascontributedtoMODMproblemsaswellastheMADMProblems.ThegeneralMODMproblemcanbedeft.edLllasfollows:Twostagescangenerallybe…     

Linguistic Anthropology in 2008: An Election-Cycle Guide

ABSTRACT   Loosely following the structure of the U.S. election cycle, I identify some of the more important institutions and events that have recently served as venues for field-building scholarly practices and processes in linguistic anthropology. I examine various trends and concerns animating recent publications on language and social life. I discuss the ongoing impact on the field of recent major works that attempt to codify methodological and theoretical approaches to the intersection of language and society. I also consider some of linguistic anthropology's emergent ventures, including new collaborative projects and new proposals for interdisciplinary work. Finally, I discuss some of the political implications of academic specialization, disciplinary boundaries, and impending "generational shift," both in the subdiscipline and the academy generally. I close by raising questions about future directions and possibilities for research in linguistic anthropology and other interdisciplinary enterprises. [Keywords: linguistic anthropology, interdisciplinarity, linguistic ideology, semiotic practices, linguistic variation]     

Fuzzy bioengineering models

A classical quantitative (analytical and/or statistical) analysis is not appropriate for some ill-defined and/or very complex bioengineering problems. Therefore, a new form of analysis using fuzzy mathematics has been developed. The fuzzy model can utilize semiqualitative and linguistic data which are to a certain level inconsistent. Bioengineering interpretation of the basic concept of fuzzy mathematics is given together with a numerical algorithm for a fuzzy evaluation of a model. A simple fuzzy model of a fermentor is studied in detail.     

Studies on mixing in horizontal rotating tubular bioreactor

In previous investigations on mixing in a horizontal rotating tubular bioreactor (HRTB) the structured “spiral flow” model was developed which contained four adjustable parameters [1, 2]. In order to incorporate the mixing model in a semifundamental scale-up procedure it was necessary to make a relation between the adjustable model parameters and process parameters of the bioreactor expressed as dimensionless numbers. Mathematical equations which relate adjustable model parameters with dimensionless numbers were developed by non-linear and surface regression methods. These equations were applied to develop the prediction systems for adjustable model parameters. In total, nine systems of equations for the prediction of the adjustable model parameters were established and examined by simulation. Three of them (SC-2, SC-6 and SC-9) were selected as adequate to describe the mixing performance of HRTB in a wide range of process conditions.     

Neuro-fuzzy decision trees

Fuzzy decision trees are powerful, top-down, hierarchical search methodology to extract human interpretable classification rules. However, they are often criticized to result in poor learning accuracy. In this paper, we propose Neuro-Fuzzy Decision Trees (N-FDTs); a fuzzy decision tree structure with neural like parameter adaptation strategy. In the forward cycle, we construct fuzzy decision trees using any of the standard induction algorithms like fuzzy ID3. In the feedback cycle, parameters of fuzzy decision trees have been adapted using stochastic gradient descent algorithm by traversing back from leaf to root nodes. With this strategy, during the parameter adaptation stage, we keep the hierarchical structure of fuzzy decision trees intact. The proposed approach of applying backpropagation algorithm directly on the structure of fuzzy decision trees improves its learning accuracy without compromising the comprehensibility (interpretability). The proposed methodology has been validated using computational experiments on real-world datasets.     

On-line state recognition in a yeast fed-batch culture using error vectors

The physiological states with respect to cell growth and ethanol production in a yeast fed-batch culture expressed in linguistic form could be recognized on-line by fuzzy inferencing based on error vectors. The error vector was newly defined here in a macroscopic elemental balance equation. The physiological states for cell growth and ethanol production were characterized by error vectors using many experimental data from fed-batch cultures. Fuzzy membership functions were constructed from the frequency distributions of the error vectors and state recognition was performed by fuzzy inferencing. In particular, an unusual physiological state for a yeast cultivation, in which aerobic ethanol production was accompanied by very low cell growth, could be recognized accurately. According to the results of the state recognition, an energy parameter, the P/O ratio in the metabolic reaction model was adaptively estimated, and the cell growth was successfully evaluated with the estimated P/O. (c) 1995 John Wiley & Sons, Inc.     

Exciton interactions in synthetic porphyrin dimers

The Soret absorption spectra of six synthetic rigid porphyrin dimers whose crystal structures have been determined are simulated using simple exciton theory. The objective is to test the validity of the point dipole and associated approximations; the electronic interaction parameters are thus calculated using data obtained from the monomer spectra, with no adjustable parameters. Satisfactory agreement between theory and experiment is obtained for one class of dimers but not for a second. This poses a challenge for semiempirical electronic structure methods as to whether improvements over the point dipole calculations can be obtained.     

Generation of deviation parameters for amino acid singlets, doublets and triplets from three-dimentional structures of proteins and its implications for secondary structure prediction from amino acid sequences

We present a new method, secondary structure prediction by deviation parameter (SSPDP) for predicting the secondary structure of proteins from amino acid sequence. Deviation parameters (DP) for amino acid singlets, doublets and triplets were computed with respect to secondary structural elements of proteins based on the dictionary of secondary structure prediction (DSSP)-generated secondary structure for 408 selected non-homologous proteins. To the amino acid triplets which are not found in the selected dataset, a DP value of zero is assigned with respect to the secondary structural elements of proteins. The total number of parameters generated is 15,432, in the possible parameters of 25,260. Deviation parameter is complete with respect to amino acid singlets, doublets, and partially complete with respect to amino acid triplets. These generated parameters were used to predict secondary structural elements from amino acid sequence. The secondary structure predicted by our method (SSPDP) was compared with that of single sequence (NNPREDICT) and multiple sequence (PHD) methods. The average value of the percentage of prediction accuracy for a helix by SSPDP, NNPREDICT and PHD methods was found to be 57%, 44% and 69% respectively for the proteins in the selected dataset. For b-strand the prediction accuracy is found to be 69%, 21% and 53% respectively by SSPDP, NNPREDICT and PHD methods. This clearly indicates that the secondary structure prediction by our method is as good as PHD method but much better than NNPREDICT method.     

A Fully Verified Theoretical Analysis of Contact‐Mode Triboelectric Nanogenerators as a Wearable Power Source

Harvesting mechanical energy from human activities by triboelectric nanogenerators (TENGs) is an effective approach for sustainable, maintenance‐free, and green power source for wireless, portable, and wearable electronics. A theoretical model for contact‐mode triboelectric nanogenerators based on the principles of charge conservation and zero loop‐voltage is illustrated. Explicit expressions for the output current, voltage, and power are presented for the TENGs with an external load of resistance. Experimental verification is conducted by using a laboratory‐fabricated contact‐mode TENG made from conducting fabric electrodes and polydimethylsiloxane/graphene oxide composite as the dielectric layer. Excellent agreements of the output voltage, current, and power are demonstrated between the theoretical and experimental results, without any adjustable parameters. The effects of the moving speed on output voltage, current, and power are illustrated in three cases, that is, the motion with constant speed, the sinusoidal motion cycles, and the real walking cycles by human subject. The fully verified theoretical model is a very powerful tool to guide the design of the device structure and selection of materials, and optimization of performance with respect to the application conditions of TENGs.     

Generation of deviation parameters for amino acid singlets, doublets and triplets from three-dimensional structures of proteins and its implications for secondary structure prediction from amino acid sequences

We present a new method, secondary structure prediction by deviation parameter (SSPDP) for predicting the secondary structure of proteins from amino acid sequence. Deviation parameters (DP) for amino acid singlets, doublets and triplets were computed with respect to secondary structural elements of proteins based on the dictionary of secondary structure prediction (DSSP)-generated secondary structure for 408 selected nonhomologous proteins. To the amino acid triplets which are not found in the selected dataset, a DP value of zero is assigned with respect to the secondary structural elements of proteins. The total number of parameters generated is 15,432, in the possible parameters of 25,260. Deviation parameter is complete with respect to amino acid singlets, doublets, and partially complete with respect to amino acid triplets. These generated parameters were used to predict secondary structural elements from amino acid sequence. The secondary structure predicted by our method (SSPDP) was compared with that of single sequence (NNPREDICT) and multiple sequence (PHD) methods. The average value of the percentage of prediction accuracy for αhelix by SSPDP, NNPREDICT and PHD methods was found to be 57%, 44% and 69% respectively for the proteins in the selected dataset. For Β-strand the prediction accuracy is found to be 69%, 21% and 53% respectively by SSPDP, NNPREDICT and PHD methods. This clearly indicates that the secondary structure prediction by our method is as good as PHD method but much better than NNPREDICT method.     

Cyclic coordinate descent: A robotics algorithm for protein loop closure

In protein structure prediction, it is often the case that a protein segment must be adjusted to connect two fixed segments. This occurs during loop structure prediction in homology modeling as well as in ab initio structure prediction. Several algorithms for this purpose are based on the inverse Jacobian of the distance constraints with respect to dihedral angle degrees of freedom. These algorithms are sometimes unstable and fail to converge. We present an algorithm developed originally for inverse kinematics applications in robotics. In robotics, an end effector in the form of a robot hand must reach for an object in space by altering adjustable joint angles and arm lengths. In loop prediction, dihedral angles must be adjusted to move the C-terminal residue of a segment to superimpose on a fixed anchor residue in the protein structure. The algorithm, referred to as cyclic coordinate descent or CCD, involves adjusting one dihedral angle at a time to minimize the sum of the squared distances between three backbone atoms of the moving C-terminal anchor and the corresponding atoms in the fixed C-terminal anchor. The result is an equation in one variable for the proposed change in each dihedral. The algorithm proceeds iteratively through all of the adjustable dihedral angles from the N-terminal to the C-terminal end of the loop. CCD is suitable as a component of loop prediction methods that generate large numbers of trial structures. It succeeds in closing loops in a large test set 99.79% of the time, and fails occasionally only for short, highly extended loops. It is very fast, closing loops of length 8 in 0.037 sec on average.