Fuzzy cluster analysis by simulated annealing

In the fuzzy clustering analysis, this paper puts forward a new algorithm to determine two objects similarity degree, the new algorithm is that the Angle cosine method and the hamming distance method bond together based on a weight synthesis. The characteristic of similarity algorithm is that it can adjust the weight coefficient according to the size of values calculated by the Angle cosine method and the hamming distance method alone; In the fuzzy pattern recognition, in order to avoid happening of "miss recognition", the paper also proposes a new kind of closeness degree which is combined by two closeness degree based on a certain weight, and it can adaptively adjust each weight according to the characteristics of the characteristic value of mode; Finally, a typical application example of fuzzy clustering analysis in the fuzzy pattern recognition is presented to illustrate the effectiveness of the two new algorithms.

There are some problems in the active participation of college students in ideological and mental health support services in China, such as low attention, low participation, and high data redundancy. Based on this, this paper studies the active participation of college students' ideological and mental health support service based on fuzzy cluster analysis algorithm. Compared with the disadvantages of the current mainstream discrete optimization analysis models on mental health (such as high-dimensional enterprise model, Dajiaweikang model, and short-range group control model), which need to set the known data gradient interval, this paper creatively adopts the fuzzy cluster analysis algorithm, based on the characteristics of different types of college students' ideological and mental health problems. Combined with the improved star discrete analysis model, this paper constructs the active participatory evaluation strategy of college students' ideological and mental health support services. On this basis, the model can not only record and store the participatory data of ideological and mental health support for students of different grades but also match and track different types of data based on special framework conditions, so as to achieve numerical normal analysis and directional matching for the data coupling mode of college students' ideological and mental health support services. On the other hand, the Planck constant factor is used to classify different types of ideological and psychological factor data, and combined with the idea of fuzzy clustering, the hierarchical analysis and quantitative calibration of different types of data groups are realized, so as to improve the reliability and authenticity of the active participation in college students' mental health support services. The results show that this star discrete analysis model can analyze the active participation of college students' ideological and mental health support services according to the data matching degree of different levels and can effectively improve the analysis efficiency of data vectors. Compared with the traditional research methods on the active participation of college students' ideological and mental health support services, this method can realize the matching and tracking of different types of data, so as to make a numerical and normal analysis on the data coupling mode of college students' ideological and mental health support services.

A detailed comparison of six multivariate algorithms is presented to analyze and generate Raman microscopic images that consist of a large number of individual spectra. This includes the segmentation algorithms for hierarchical cluster analysis, fuzzy C-means cluster analysis, and k-means cluster analysis and the spectral unmixing techniques for principal component analysis and vertex component analysis (VCA). All algorithms are reviewed and compared. Furthermore, comparisons are made to the new approach N-FINDR. In contrast to the related VCA approach, the used implementation of N-FINDR searches for the original input spectrum from the non-dimension reduced input matrix and sets it as the endmember signature. The algorithms were applied to hyperspectral data from a Raman image of a single cell. This data set was acquired by collecting individual spectra in a raster pattern using a 0.5-μm step size via a commercial Raman microspectrometer. The results were also compared with a fluorescence staining of the cell including its mitochondrial distribution. The ability of each algorithm to extract chemical and spatial information of subcellular components in the cell is discussed together with advantages and disadvantages.

Cluster analysis by simulated annealing

Validating and improving the correction of ocular artifacts in electro-encephalography

A new bi-objective green medicine supply chain network design under fuzzy environment: Hybrid metaheuristic algorithms

A combined strategy of an improved simulated annealing (SA) algorithm and genetic algorithm is presented, with the goal of reducing the computational expenses. The improvements made on the SA algorithm include two parts, i.e., the adaptive regulating for the step vector, and the dynamic testing for the equilibrium of the Metropolis process. The proposed strategy has both the advantage of SA algorithm, the ability to avoid being trapped in a local optimum, and that of genetic algorithm, the ability to use the information about the searched states for the next iteration. A practical application on geometry optimization of pole shoes in large salient pole synchronous generators is effectively implemented using the strategy. The numerical results show that the number of iterations used by executing the combined strategy are only about 75% of those by executing basic SA algorithm.

Fuzzy clustering analysis algorithm has good ability to solve fuzzy problems. Rough clustering analysis has good ability to solve these problems its prior knowledge is uncertain. But in the real world, there are many problems that not only are fuzzy but also are rough and uncertain; the paper combines the idea of these two algorithms. In order to improve correction of clustering, it imports attributes reduction algorithm to get importance of each attribute, and dynamically changes attribute weight by the importance. The new algorithm firstly computes fuzzy membership degree of every object and then estimates the object that belongs to lower approximation or upper approximation of one cluster. In the analysis process, the paper provides a new way to get the cluster centers, combining fuzzy and rough theory. From experiments of four UCI data sets, it is proved that the new algorithm is better effective.

Logical character dependency is a major conceptual and methodological problem in phylogenetic inference of morphological data sets, as it violates the assumption of character independence that is common to all phylogenetic methods. It is more frequently observed in higher-level phylogenies or in data sets characterizing major evolutionary transitions, as these represent parts of the tree of life where (primary) anatomical characters either originate or disappear entirely. As a result, secondary traits related to these primary characters become "inapplicable" across all sampled taxa in which that character is absent. Various solutions have been explored over the last three decades to handle character dependency, such as alternative character coding schemes and, more recently, new algorithmic implementations. However, the accuracy of the proposed solutions, or the impact of character dependency across distinct optimality criteria, has never been directly tested using standard performance measures. Here, we utilize simple and complex simulated morphological data sets analyzed under different maximum parsimony optimization procedures and Bayesian inference to test the accuracy of various coding and algorithmic solutions to character dependency. This is complemented by empirical analyses using a recoded data set on palaeognathid birds. We find that in small, simulated data sets, absent coding performs better than other popular coding strategies available (contingent and multistate), whereas in more complex simulations (larger data sets controlled for different tree structure and character distribution models) contingent coding is favored more frequently. Under contingent coding, a recently proposed weighting algorithm produces the most accurate results for maximum parsimony. However, Bayesian inference outperforms all parsimony-based solutions to handle character dependency due to fundamental differences in their optimization procedures-a simple alternative that has been long overlooked. Yet, we show that the more primary characters bearing secondary (dependent) traits there are in a data set, the harder it is to estimate the true phylogenetic tree, regardless of the optimality criterion, owing to a considerable expansion of the tree parameter space. [Bayesian inference, character dependency, character coding, distance metrics, morphological phylogenetics, maximum parsimony, performance, phylogenetic accuracy.].

The N-Queen problem is a classical combinatorial optimization and constraint satisfaction problem that involves placing N queens on an N × N chessboard such that no two queens threaten each other. Due to its exponential search space and increasing computational complexity with larger board sizes, the problem serves as an important benchmark for evaluating the efficiency of heuristic and metaheuristic optimization techniques. This study presents a comprehensive performance analysis of the Simulated Annealing (SA) optimization approach for solving the N-Queen problem across varying board dimensions. The proposed methodology models the N-Queen problem as an optimization task by defining an objective function that minimizes the number of conflicting queen pairs. Simulated Annealing is employed to iteratively explore the solution space, allowing probabilistic acceptance of suboptimal solutions to escape local minima. Key parameters, including initial temperature, cooling schedule, and termination criteria, are systematically tuned to evaluate their impact on convergence speed and solution quality. Experimental results demonstrate that the SA-based approach effectively finds valid solutions for large values of N with high success rates and reduced computational effort compared to exhaustive and deterministic methods. Performance metrics such as solution accuracy, convergence time, number of iterations, and conflict reduction rate are analyzed and discussed. The results indicate that Simulated Annealing offers a robust and scalable solution for the N-Queen problem, particularly for higher-dimensional instances where traditional methods become impractical. Overall, this study highlights the suitability of Simulated Annealing as an efficient metaheuristic for solving complex combinatorial optimization problems and provides insights into its performance characteristics when applied to the N-Queen problem.

Aiming at a series of problems existing in volleyball, based on radial fuzzy neural network theory, the optimized simulation clustering analysis algorithm is used to monitor and analyze volleyball. By analyzing the feature weights of nodes at different stages, the optimal radial fuzzy neural network was constructed, which was combined with the simulation clustering algorithm to obtain the relevant optimization model describing volleyball. The accuracy of the optimization model is verified by comparing with the original model. The results show that with the increase of response, the response curves of different algorithms show fluctuation. Among them, the fluctuation range of MPDR (minimum power distortionless response) algorithm is larger, and the value of the curve obtained by MVDR (minimum variance distortionless response) algorithm differs greatly from that of the optimization algorithm at some key nodes, while the beam changing chart obtained by the optimization algorithm can better reflect the changing trend of the beam. Model indexes under different algorithms are different. When the number of iterative steps is less than 30, indexes under different algorithms are all greater than the standard value. When the number of iterations is more than 30, the indexes under different algorithms are all less than the standard value. Through verification, it can be seen that the original model can only describe the first stage of volleyball, while the optimization model can describe the whole process of volleyball. It shows that the optimization model can be used to describe and analyze volleyball-related data. And the algorithm can be used to better predict and analyze volleyball, and the analysis results can provide relevant guidance for volleyball. The optimization model provides basis and theoretical support for the application of volleyball simulation clustering algorithm, so as to better promote volleyball and better guide the movement.

Red-light running violation prediction using observational and simulator data

Summary1. Ecological specialization is a unifying concept in the biological sciences. While there are reliable ways to characterize specificity at individual and community levels, the evaluation of population and species‐level measures is lacking. There is a need for such assessments given that populations and species are the relevant scales for most ecological and evolutionary processes.2. Using examples of simulated and empirical data sets of bipartite networks representing a continuum of biological interactions, we evaluate six indices of specificity in terms of their robustness to incomplete sampling and information they extract from data.3. Robustness differed between the measures and in their ability to differentiate specialists and generalists along a full continuum. On the empirical data sets, indices were less separated by their informativity than on the simulated data sets, which may be due to the heterogeneity of the former.4. Based on these different evaluations for species‐level (or population‐level) specificity, we recommend the use of Resource range when no quantitative data are available and Paired Difference Index otherwise. These results will assist both applied and fundamental researchers in the characterization and interpretation of species specificity.

Two-way satellite time and frequency transfer (TWSTFT) data are typically recorded on Monday, Wednesday and Friday. This produces an unevenly spaced time series on which it is difficult to perform an accurate two-sample variance analysis. We have investigated the effect of uneven data spacing on the computation of σx(τ) [1-2]. Evenly spaced simulated data sets were generated for noise processes ranging from white phase modulation to random walk frequency modulation. σx(τ) was then calculated for each noise type. Data were subsequently removed from each simulated data set to create two unevenly spaced sets with average intervals of 2,8 and 3,6 days. These sparse sets correspond to typical TWSTFT data patterns. σx(τ) was then calculated for each sparse data set using two different approaches. First, the missing data points were replaced by linear interpolation and σx(τ) calculated from this now full data set. The second approach ignored the fact that the data were unevenly spaced and calculated σx(τ) as if the data were equally spaced with average spacing of 2,8 or 3,6 days. The impact of uneven data spacing on the results of these two approaches is significant and is discussed. Finally, techniques are presented for correcting errors caused by uneven data spacing in simulated TWSTFT data sets, and the appropriate technique is applied to a real data set.

In support of Phase A work on the proposed Hyperspectral Environment and Resource Observer (HERO) mission, the sensitivity of chlorophyll concentrations derived from vegetation red-edge analysis to a spectrally dependent error in the radiometric calibration of the hyperspectral data is investigated. A typical ground-based reflectance spectrum taken from a boreal forest Aspen leaf is converted to top-of-atmosphere (TOA) radiance using the MODTRAN atmospheric correction model as implemented in the Imaging Spectrometer Data Analysis System (ISDAS). The resulting spectrum is then subjected to a randomly generated Spectral Gain Error (SGE). This process is repeated a statistically significant number of times to produce a simulated data set. By varying the magnitude of the SGE, several simulated data sets are produced representing different levels of relative calibration accuracies in the spectral domain. The simulated TOA data sets are then converted back to ground-based reflectance, once again using MODTRAN. For each pixel in the resulting data sets, red-edge parameters are determine using an Inverted Gaussian (IG) technique (red-edge inflection point - lambdap, reflectance minimum - lambdao, and sigma = lambdap-lambdao) as well as a couple of commonly applied Vegetation Indices (R740/R720 and R710/R760)- Each of these parameters results in a distribution of results, the width of which is dependent on the magnitude of the SGE applied to the data set providing the relationship between the parameters and the SGE. In order to tie these results to chlorophyll concentration levels, the PROSPECT Vegetation Model is used to invert the original spectrum. The sensitivity of each of the indices to the chlorophyll concentration is determined by varying its value in the PROSPECT model while holding the other three model parameters constant. Using these relationships, the sensitivity of the chlorophyll concentrations to the SGE is determined. A specification of the acceptable error in the chlorophyll concentration levels would then dictate the required level of accuracy in the spectral gain calibration. The relationship between the SGE and the chlorophyll concentration retrieval based upon any of the five investigated red-edge parameters is shown to be directly proportional.