Self-organizing Map Neural Network Research Articles

Unsupervised Clustering-Assisted Method for Consensual Quantitative Analysis of Methanol-Gasoline Blends by Raman Spectroscopy.

Methanol-gasoline blends have emerged as a promising and environmentally friendly bio-fuel option, garnering widespread attention and promotion globally. The methanol content within these blends significantly influences their quality and combustion performance. This study explores the qualitative and qualitative analysis of methanol-gasoline blends using Raman spectroscopy coupled with machine learning methods. Experimentally, methanol-gasoline blends with varying methanol concentrations were artificially configured, commencing with initial market samples. For qualitative analysis, the partial least squares discriminant analysis (PLS-DA) model was employed to classify the categories of blends, demonstrating high prediction performance with an accuracy of nearly 100% classification. For the quantitative analysis, a consensus model was proposed to accurately predict the methanol content. It integrates member models developed on clustered variables, using the unsupervised clustering method of the self-organizing mapping neural network (SOM) to accomplish the regression prediction. The performance of this consensus model was systemically compared to that of the PLS model and uninformative variable elimination (UVE)-PLS model. Results revealed that the unsupervised consensus model outperformed other models in predicting the methanol content across various types of methanol gasoline blends. The correlation coefficients for prediction sets consistently exceeded 0.98. Consequently, Raman spectroscopy emerges as a suitable choice for both qualitative and quantitative analysis of methanol-gasoline blend quality. This study anticipates an increasing role for Raman spectroscopy in analysis of fuel composition.

A Health Management Technology Based on PHM for Diagnosis, Prediction of Machine Tool Servo System Failures

The computer numerically controlled (CNC) system is the key functional component of CNC machine tool control systems, and the servo drive system is an important part of CNC systems. The complex working environment will lead to frequent failure of servo drive systems. Taking effective health management measures is the key to ensure the normal operation of CNC machine tools. In this paper, the comprehensive effect of fault prediction and fault diagnosis is considered for the first time, and a health management system for machine tool servo drive systems is proposed and applied to operation and maintenance management. According to the data collected by the system and related indicators, the technology can predict the state trend of equipment operation, identify the hidden fault characteristics in the data, and further diagnose the fault types. A health management system mainly includes fault prediction and fault diagnosis. The core of fault prediction is the gated recurrent unit (GRU). The attention mechanism is introduced into a GRU neural network, which can solve the long-term dependence problem and improve the model performance. At the same time, the Nadam optimizer is used to update the model parameters, which improves the convergence speed and generalization ability of the model and makes it suitable for solving the prediction problem of large-scale data. The core of fault diagnosis is the self-organizing mapping (SOM) neural network, which performs cluster analysis on data with different characteristics, to realize fault diagnosis. In addition, feature standardization and principal component analysis (PCA) are introduced to balance the influence of different feature scales, enhance the feature of fault data, and achieve data dimensionality reduction. Compared with the other two algorithms and their improved versions, the superiority of the health management system with high-dimensional data and the enhancement effect of fault identification are verified. The relative relationship between fault prediction and diagnosis is further revealed, and the adjustment idea of the production plan is provided for decision makers. The rationality and effectiveness of the system in practical application are verified by a series of tests of fault data sets.

Quantum self-organizing feature mapping neural network algorithm based on Grover search algorithm

Self-organizing feature mapping neural network is a typical unsupervised neural network algorithm, which is often used for clustering analysis and data compression. As the amount of data increases, the time consumption required by the algorithm becomes increasingly large, which becomes a new challenge. To address this issue, a quantum self-organizing feature mapping neural network is proposed in this paper. This algorithm provides a method to obtain the similarity between samples and neurons based on quantum phase estimation and demonstrates the scheme to obtain winning neurons by Grover algorithm. By utilizing the superposition of quantum, the algorithm achieves parallel computing. The time complexity analysis indicates that the proposed algorithm is exponentially faster than the classical counterpart. The quantum circuit has been devised, while numerical simulation and experiment on a heart disease dataset have been conducted programming within the Qiskit framework. Both have verified the feasibility of the algorithm. Moreover, an application of classification has been developed based on the trained self-organizing feature mapping neural network, which demonstrates the effectiveness of the proposed algorithm.

Research and application of safety monitoring technology of distribution automation based on SOM neural network

Abstract For automatic situation monitoring in power distribution safety monitoring, data features are mainly extracted by single hidden layer neural network, which makes the standard error of monitoring results larger. Therefore, the research and application of power distribution automation safety monitoring technology based on SOM neural network are proposed. Preprocess the power grid, collect distribution operation data, set multi-dimensional monitoring nodes according to the collected data, build a distribution operation status monitoring model and analyze the data fusion technology of distribution automation safety monitoring according to the model. On this basis, the distribution automation safety monitoring system is defined, the output node correction weight and the reverse output node correction weight are calculated, the SOM neural network identification model is constructed and the research and application of the distribution automation safety monitoring technology are completed under the action of the gravitational function between individuals within the target time. The experimental results show that the change curve of the number of vulnerabilities and the actual number of false positives is consistent, the number of vulnerabilities is small and the monitoring results are more accurate; The state of the safety monitoring equipment of distribution automation is normal. After applying the method in this paper, the change curve is consistent with the actual value, and the delay time is less than 15 ms.

A hybrid approach of deep learning to forecast financial performance: from unsupervised to supervised

The financial performance of a listed company is a common concern for shareholders, creditors, employees, securities analysts, and the government. Measuring and forecasting financial performance informs stakeholders about a company's overall well-being. In this study, we propose a hybrid approach that combines grey relation analysis, Self-Organized Mapping (SOM) neural network, and convolutional neural network to assess the financial performance of listed companies. Grey relation analysis measures financial performance, SOM neural network clusters, and convolutional neural network forecasts. Compared to other models, the hybrid convolutional neural network model has a better predictive effect, accurately forecasting the financial status of listed companies. Findings also reveal that 70.93 percent of listed companies in agriculture, forestry, husbandry, and fisheries have a poor financial status.

Experimental investigation on acoustic emission precursor of rockburst based on unsupervised machine learning method

The key to achieving rockburst warning lies in the understanding of rockburst precursors. Considering the correlation characteristics of rockburst acoustic emission (AE) parameters, a self-organizing map neural network (SOMNN) based method for rockburst precursor inversion was proposed. The feature of this method lies in a cyclic data segmentation iteration process based on the thinking of “interference signal screening”, “key signal extraction”, and “precursor signal inversion”. The rationality of this method has been verified in three groups of rockburst experiments. The results revealed that rockburst AE precursor signals consist of a series of signals characterized by long duration, high energy, low average frequency, high energy amplitude, and low peak frequency. Subsequently, potential value in long term rockburst warning of the precursor obtained in this study was shown via the comparison of conventional precursors. Finally, a preliminary interpretation for rockburst precursor was proposed under the framework of AE parameters physical significance, and it is revealed that AE precursor signals are likely linked to the creation of large-scale tensile cracks before rockburst.

Seismic fault identification in coal mines based on the self-organizing map-gray wolf optimizer-support vector machine algorithm

Accurate fault identification in coal mines is important to improve mine safety and economic benefits. We compare various intelligent algorithms for data preprocessing and optimization and analyze the construction methods of seismic attribute data sets and the performance of intelligent optimization algorithms using fault identification accuracy as the discrimination index to find a better combined model for seismic fault identification. First, the training data set is constructed by mining the fault and nonfault information revealed by the roadway. The distribution characteristics of the seismic attribute data indicate similarities among them, and they are nonlinearly separable. Directly using the attributes to construct the data set, the accuracy of fault identification using the support vector machine (SVM) model is 78.41%. Principal component analysis (PCA) and self-organizing mapping (SOM) neural networks are used to extract effective information and then combined with the SVM classification model, and the accuracy of fault identification is 83.82% and 87.47%, respectively. Compared with the original data and PCA dimensionality reduction data, the accuracy of fault detection is improved by 9.06% and 3.66%, respectively, indicating that SOM can effectively improve the accuracy of fault detection by eliminating similar attributes and reducing the weight of redundant information. Then, through a fixed attribute data set, genetic algorithm (GA), particle swarm optimization (PSO), and gray wolf optimizer (GWO) intelligent optimization algorithms are used to find the optimal kernel function parameter and penalty parameter of the SVM classifier. The accuracy rate of the SOM-GWO-SVM model reaches 91.12%, compared with the SOM-PSO-SVM and SOM-GA-SVM, and the model accuracy is increased by 5.2% and 5.61%, respectively. Compared with PSO and GA, the GWO algorithm has a better global search ability. The identification result of the SOM-GWO-SVM model is closest to the actual fault exposure, especially for the identification of “short” faults and associated faults, which has obvious advantages over the traditional manual interpretation in terms of efficiency and accuracy.

Modeling Personalized Smart Teaching for Learner Needs

Abstract With the deep integration of the Internet and education, the personalized development of education has become a new trend in education, and it also increasingly emphasizes the learner’s subject position in learning. In this study, a smart teaching model for learners’ individuality is developed by integrating WEB data mining technology, SOM neural network, and multiple recommendation mechanisms. The model achieves personalized recommendations for learning resources through the collection of user characteristics and then according to the recommendation algorithm. Then, using college English courses at Zhengzhou Shengda University as an example, the SOM neural network is utilized to diagnose the teaching cognition of the experiment. The experimental results show that the SOM neural network cognitive diagnosis results in a high judgment rate, with a high judgment rate of 84.842%. It has certain feasibility in teaching small sample diagnostics. In terms of efficiency, the time of cognitive diagnosis can be controlled within 1 second, which is real-time in teaching applications. The significance test of students’ performance after the experiment shows that the personalized wisdom teaching model constructed in this paper has a significant effect on improving teaching performance.

Detection method of abnormal state of power transformer based on SOM neural network

Detection method of abnormal state of power transformer based on SOM neural network

Self-Organizing Mapping Neural Network Implementation Based on 3-D NAND Flash for Competitive Learning

Self-Organizing Mapping Neural Network Implementation Based on 3-D NAND Flash for Competitive Learning

Integrated transformer condition assessment based on SOM neural network evidence cloud element model

Integrated transformer condition assessment based on SOM neural network evidence cloud element model

Variations in boundary layer stability across Antarctica: a comparison between coastal and interior sites

Abstract. The range of boundary layer stability profiles, from the surface to 500 m a.g.l. (above ground level), present in radiosonde observations from two continental-interior (South Pole Station and Dome Concordia Station) and three coastal (McMurdo Station, Georg von Neumayer Station III, and Syowa Station) Antarctic sites, is examined using the self-organizing maps (SOMs) neural network algorithm. A wide range of potential temperature profiles is revealed, from shallow boundary layers with strong near-surface stability to deeper boundary layers with weaker or near-neutral stability, as well as profiles with weaker near-surface stability and enhanced stability aloft, above the boundary layer. Boundary layer regimes were defined based on the range of profiles revealed by the SOM analysis; 20 boundary layer regimes were identified to account for differences in stability near the surface as well as above the boundary layer. Strong, very strong, or extremely strong stability, with vertical potential temperature gradients of 5 to in excess of 30 K per 100 m, occurred more than 80 % of the time at South Pole and Dome Concordia in the winter. Weaker stability was found in the winter at the coastal sites, with moderate and strong stability (vertical potential temperature gradients of 1.75 to 15 K per 100 m) occurring 70 % to 85 % of the time. Even in the summer, moderate and strong stability is found across all five sites, either immediately near the surface or aloft, just above the boundary layer. While the mean boundary layer height at the continental-interior sites was found to be approximately 50 m, the mean boundary layer height at the coastal sites was deeper, around 110 m. Further, a commonly described two-stability-regime system in the Arctic associated with clear or cloudy conditions was applied to the 20 boundary layer regimes identified in this study to understand if the two-regime behavior is also observed in the Antarctic. It was found that moderate and strong stability occur more often with clear- than cloudy-sky conditions, but weaker stability regimes occur almost equally for clear and cloudy conditions.

A Self-Organizing feature Map neural network for compton suppression in phoswich detector

Scintillation phoswich detectors can be used for particle discrimination and Compton suppression, and their function is based on pulse shape discrimination (PSD) methods. The conventional PSD methods mainly including Rise Time Discrimination (RTD), Rise and Decay time discrimination (R&D), Constant Time Discrimination (CTD), etc., but linear PSD methods have their limitations in processing pulse signals. Neural network tools can achieve nonlinear output by adjusting the weights between neurons during the training, which are suitable for identifying nonlinear signals generated by detectors. The self-organizing feature mapping (SOM) is the most commonly used clustering neural network, which can cluster signals based on their similarity through competitive training of neural networks without prior data. In this study, 3 SOM networks with different size are trained, and after comparison with the single LaBr3:Ce detector, the total accuracy of the phoswich detector using the SOM neural network for discrimination reaches over 99.5%, which is a 7.59% increase in accuracy compared to the RTD method, and the peak-to-total ratio (P/T) is improved by 1.07%.

An iteration-free hierarchical method for the energy management of multiple-microgrid systems with renewable energy sources and electric vehicles

Recent trends in the increasing penetration of renewable energy generation and the increasingly massive integration of electric vehicles (EVs) into multiple-microgrid (MMG) systems pose tremendous challenges to the effective energy management of these systems. However, the solutions of current energy management methods have the disadvantage of excessively limited robustness against uncertain fluctuations in renewable energy generation, and existing hierarchical EV scheduling methods are solved via an iterative process that is prohibitively time-consuming for real-time practical applications. This work addresses the abovementioned issues by constructing an improved data-driven polyhedral uncertainty set based on modified self-organizing feature map neural networks that perform data clustering. In addition, an iteration-free hierarchical framework is proposed for conducting the tri-layer energy management of MMGs with the massive integration of EVs. Specifically, the global positive power factor of EV aggregation is proposed to formulate a set of constraints into the upper-layer model of the tri-layer framework, which can guarantee the feasibility of the middle-and-lower layer models. The value of this factor is determined using a multi-objective optimization algorithm that makes a trade-off between the feasible range of the output of EV aggregators and the degree to which the trip demands of EVs are violated. The superiority and effectiveness of the proposed iteration-free robust hierarchical energy management method for MMGs are verified via simulations involving a practical MMG under high penetrations of renewable energy generation and massive integration of EVs.

Diagnosing injection-production system faults in the same well using the rough set-LVQ neural network.

This study proposed a reverse calculation model of the unique rod pump injection and production system structures in the same well to diagnose and resolve defects, after which dynamometer diagrams of the system production and injection pumps were drawn. The invariant moment feature method was applied to identify seven such characteristics in the injection pump power graph, establishing a downhole system for fault diagnosis in rod pump injection and production systems in the same well using Rough Set(RS)-Learning Vector Quantization(LVQ). On the premise of keeping the classification ability unchanged, the Self-Organizing Map(SOM) neural network was used to discretize the original feature data, while RS theory was employed for attribute reduction. After establishing the LVQ fault diagnosis subsystem, the reduced decision table was entered for learning and training. The test results confirmed the efficacy and accuracy of this method in diagnosing downhole faults in rod pump injection-production systems in the same well. After comparing the test results with the actual working conditions, it can be seen that the rod pump injection-production diagnosis system based on RS-LVQ designed in this paper has a recognition rate of 91.3% for fault types, strong recognition ability, short diagnosis time, and A certain practicality. However, the research object of fault diagnosis in this paper is a single fault, and the actual downhole fault situation is complex, and there may be two or more fault types at the same time, which has certain limitations.

A 50-year cycle of sea surface temperature regulates decadal precipitation in the tropical and South Atlantic region

Northeast Brazil and Western Africa are two regions geographically separated by the Atlantic Ocean, both home to vulnerable populations living in semi-arid areas. Atlantic Ocean modes of variability and their interactions with the atmosphere are the main drivers of decadal precipitation in these Atlantic Ocean coastal areas. How these low-frequency modes of variability evolve and interact with each other is key to understanding and predicting decadal precipitation. Here we use the Self-Organizing Maps neural network with different variables to unravel causality between the Atlantic modes of variability and their interactions with the atmosphere. Our study finds an 82% (p<0.05) anti-correlation between decadal rainfall in Northeast Brazil and Western Africa from 1979 to 2005. We also find three multi-decadal cycles: 1870-1920, 1920-1970, and 1970-2019 (satellite era), pointing to a 50-year periodicity governing the sea surface temperature anomalies of Tropical and South Atlantic. Our results demonstrate how Northeast Brazil and Western Africa rainfall anti-correlation was formed in the satellite era and how it might be part of a 50-year cycle from the Tropical and South Atlantic decadal variability.

Ecosystem health risk assessment of lakes in the Inner Mongolian Plateau based on the coupled AHP-SOM-CGT model

The ecosystem health risk assessment of lake basins can provide a crucial foundation and support for the sustainable development of ecosystems in the arid regions of northern China. This study adopts the Driver-Pressure-State-Impact-Response (DPSIR) framework to select evaluation indicators, and utilizes a coupled model of the Analytic Hierarchy Process (AHP)-Self-Organizing Mapping Neural Network Algorithm (SOM)-Combinatorial Game Theory Algorithm (CGT) to determine the weight values of each indicator, and ultimately calculate the Ecosystem Health Index (EHI) of a typical lake basin from 1990 to 2020. AHP and SOM are employed to determine the subjective and objective weights of the evaluation indicators, respectively. While, the CGT is used to establish a balance between these two weights. This ensures that the final weight values embody both the subjective preferences of the decision-makers and the objective characteristics inherent in the data. The calculations show that the EHI of the basin fluctuated from a healthy state (69.8) in 1990 to a sub-healthy state (47.0) in 2020, although the dominant factors influencing EHI varied across different stages, the decline in the Driver and Impact health indices emerged as the primary reason for the decrease in EHI. Before 2009, driven by socio-economic development, the area of irrigated land and water withdrawals in the basin increased significantly, which led to an increase in ecosystem pressures, resulting in a rapid decrease in EHI. After 2009, the increase in per capita water resources and water-saving irrigation facilities reduced the pressure on the ecological system. the increased ecological water use and afforestation area improved the ecosystem’s status, leading to an increase in the vegetation cover within the basin and a gradual upward trend in EHI.

Characterization of movement patterns using unsupervised learning neural networks: Exploring a novel approach for monitoring athletes during sidestepping

ABSTRACT The monitoring of athletes is crucial to preventing injuries, identifying fatigue or supporting return-to-play decisions. The purpose of this study was to explore the ability of Kohonen neural network self-organizing maps (SOM) to objectively characterize movement patterns during sidestepping and their association with injury risk. Further, the network’s sensitivity to detect limb dominance was assessed. The data of 67 athletes with a total of 613 trials were included in this study. The 3D trajectories of 28 lower-body passive markers collected during sidestepping were used to train a SOM. The network consisted of 1247 neurons distributed over a 43 × 29 rectangular map with a hexagonal neighbourhood topology. Out of 61,913 input vectors, the SOM identified 1247 unique body postures. Visualizing the movement trajectories and adding several hidden variables allows for the investigation of different movement patterns and their association with joint loading. The used approach identified athletes that show significantly different movement strategies when sidestepping with their dominant or non-dominant leg, where one strategy was clearly associated with ACL-injury-relevant risk factors. The results highlight the ability of unsupervised machine learning to monitor an individual athlete’s status without the necessity to reduce the complexity of the data describing the movement.

Research on the Design of Financial Management Model Based on SOM-PNN Driven by Digital Economy

This study proposes a novel financial risk prediction methodology by harnessing the power of self-organizing mapping (SOM) neural network and probabilistic neural network (PNN). The amalgamation of SOM and PNN's advantageous characteristics is seamlessly integrated into the algorithm posited within this paper. In order to collate and prognosticate data, the SOM network employs a two-dimensional topological framework comprising of two layers of neurons. Subsequently, the PNN model expeditiously furnishes the final classification outcomes by processing the output results obtained from the SOM model. The technique developed atop this composite model offers accelerated computation, effectively mitigates the impact of noisy samples, and significantly augments model accuracy. Finally, the effectiveness of the proposed method was demonstrated through a comprehensive financial risk analysis of listed companies from 2016 to 2020. The experimental results show that the SOM-PNN method has achieved high accuracy in predicting the financial difficulties experienced by traditional companies in the selected company samples, exceeding 85%. Especially when the sample data is insufficient, its accuracy reaches 80%, surpassing other algorithms. Statement: In the modern era, financial institutions use big data to perform background analysis and review, continuously optimize, and adjust, in order to introduce quantitative analysis methods into every link of risk management as far as possible. This allows financial institutions to quickly achieve balance in the game process of risk and income, and achieve Profit maximization in local or even more space.

Mesh optimization using an improved self-organizing mechanism

As more powerful computing hardware enables higher resolution simulations, a fast and flexible mesh optimization method is becoming increasingly indispensable for Computational Fluid Dynamics (CFD), which unfortunately remains a bottleneck in the current CFD workflows. In this paper, a novel mesh optimization method based on an improved self-organizing map (SOM) neural network is proposed to improve the accuracy and efficiency of numerical simulation while maintaining constant computational cost. During an improved competitive learning procedure in SOM, the node distribution with constant connectivity rapidly matches the characteristics of the flow field, which is predicted by a Multilayer Perceptron (MLP). Based on the local element volume and flow solution variations, annealing schemes for self-adaptation of important SOM parameters are designed to ensure the convergence of the proposed algorithm. Specially, a feasible region constraint and a smoothing constraint are embedded into the node movement to avoid mesh tangling and excessive mesh skewness, and make the transition between nodes gradual. The proposed approach is applicable to various types of meshes and is easy to implement without code intrusiveness. Comparative results on benchmark examples and typical CFD examples demonstrate that the proposed method attributes to both the improvement in the computational accuracy and efficiency. It exhibits the potential to be a flexible and promising tool for rapid mesh optimization in CFD and other engineering fields.