Self-organizing Feature Map Neural Network Research Articles

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.

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.

Ecological function zoning of Nansi Lake Basin in China based on ecosystem service bundles.

Ecological function zoning is an essential means of scientific management of ecosystems. According to the characteristics of ecological function zoning, implementing zoning control is conducive to the governance and protection of the ecological environment and the maintenance of ecological sustainability. This study was conducted with the Nansi Lake Basin as the cross-section for 2018. The Integrated Valuation of Ecosystem Services and Trade-offs model was adopted to assess and measure five ecosystem services, including water yield, crop production, soil conservation, carbon storage and carbon sequestration, and habitat quality. The Self-Organizing Feature Maps neural network was applied to obtain the ecosystem service bundles, and then, the ecological function zones were divided. The results indicated that the overall spatial pattern of ecosystem services in the study zone showed a decreasing schema from east to west; There was a trade-off between supply services and support services and a synergy between supply services and regulatory services; according to the bundling results, the Nansi Lake Basin was divided into four ecological functional zones: the eastern ecological surplus zone, the central crop supply zone, the western ecological balance zone, and the lake habitat protection zone. The results showed that (1) the spatial distribution of various ecosystem services in the Nansi Lake Basin showed spatial heterogeneity and specific regional laws, showing a decreasing pattern from the east to the west as a whole, especially in soil conservation, carbon sequestration, and habitat quality. (2) According to the supply and spatial distribution of each ecosystem service, the Nansi Lake Basin was divided into four ecological functional zones: the eastern ecological surplus zone, the central crop supply zone, the western ecological balance zone, and the lake habitat protection zone. (3) For zone I, provisioning services and regulation services were in synergy. For zone II and zone III, the provisioning service had a trade-off relationship with the regulation service and the supporting service. For zone IV, supporting services were trade-offs not only with provisioning services but also with regulating services. In general, the trade-offs between ecosystem service in the Nansi Lake Basin were stronger than the synergies, and the overall benefits of ecosystem services were smaller. Relying on the perspective of the ecosystem service bundles, at the county level, this study provided an analysis of the trade-offs and synergies among ecosystem services in the Nansi Lake Basin, which helped formulate the management plan for the corresponding region and provided the appropriate recommendations for regional habitat conservation and restoration.

Incorporating ecological connectivity into ecological functional zoning: A case study in the middle reaches of Yangtze River urban agglomeration

Ecological functional zoning is critical for multiobjective sustainable management of social-ecological systems. However, the framework of ecological functional zoning needs to be further improved. In terms of the index system, the existing “element-structure–function” index system mainly considers the structural characteristics that have a negative impact on ecological processes, e.g., disturbances, while ignoring positive impacts, e.g., connectivity, which may weaken the role of key nodes in the ecosystem and is not conducive to regional ecological protection. In terms of research methodology, the widely used self-organizing feature mapping neural network (SOFM) produces a number of clusters that easily exceed expectations, thus increasing management difficulties. Therefore, there is an urgent need to explore new combining methods. Here, an improved index system combining ecological connectivity and ecological functions and a traditional index system including only ecological functions were constructed, and a zoning method combining a self-organizing feature mapping neural network and fuzzy mean clustering (SOFM-FCM) was used for functional zoning of the middle reaches of the Yangtze River urban agglomeration. The advantages of functional zoning incorporating ecological connectivity were verified by comparing the results of both zoning procedures before and after improvement. The results indicate that the study area could be divided into ecological conservation areas, biodiversity conservation areas, urban development areas, and grain production areas. The zoning schemes before and after the improved framework were reliable compared with the national planning scheme, but there was 20.6% inconsistency in the results after the improved framework. This was mainly manifested by the shift from biodiversity conservation areas to ecological conservation areas and from urban development areas to food production areas. This shift was more conducive to improving spatial continuity and landscape multifunctionality, thus maximizing ecological conservation, pushing back smart urban growth, and achieving food security. This study expands the research perspective concerned with ecological functional zoning, and these results can provide an important reference for regional ecological protection and related research.

A SOM-CNN Algorithm for NLOS Signal Identification

Aiming at the problem of non-line of sight (NLOS) signal recognition for Ultra Wide Band (UWB) positioning, we utilize the concepts of Neural Network Clustering and Neural Network Pattern Recognition. We propose a classification algorithm based on self-organizing feature mapping (SOM) neural network batch processing, and a recognition algorithm based on convolutional neural network (CNN). By assigning different weights to learning, training and testing parts in the data set of UWB location signals with given known patterns, a strong NLOS signal recognizer is trained to minimize the recognition error rate. Finally, the proposed NLOS signal recognition algorithm is verified using data sets from real scenarios. The test results show that the proposed algorithm can solve the problem of UWB NLOS signal recognition under strong signal interference. The simulation results illustrate that the proposed algorithm is significantly more effective compared with other algorithms.

Retraction Note: Visualization model of big data based on self-organizing feature map neural network and graphic theory for smart cities

Retraction Note: Visualization model of big data based on self-organizing feature map neural network and graphic theory for smart cities

Classification of Shrinking Cities in China Based on Self-Organizing Feature Map

Since the 1980s, China has been experiencing shrinking cities as part of a massive urbanization process. In recent years, the Matthew effect of factor accumulation has led to an increasingly substantial divergence in population concentration and sparseness in China. The pattern of shrinking Chinese cities has become increasingly severe. Accurate classification of shrinking cities is important for formulating policies and achieving the rational development of shrinking cities. In this study, the data of the 6th and 7th population censuses are used to investigate the shrinking Chinese cities, and prefecture-level cities are utilized as the scale of administrative units. The resident population in 130 cities decreased during the last decade. The population, economy, society, and space indicators are selected to cluster the shrinking cities through the self-organizing feature map neural network. Results show that China’s shrinking cities can be divided into four categories: (1) Sixty-two cities are characterized by a high degree of transfer dependence on the economy due to a chronic lack of population. (2) Twenty-eight cities are characterized by high urban expansion but with population loss. (3) Fourteen cities are characterized by obvious transportation and location advantages and with relatively slight population loss. (4) Twenty-six cities have good industrial development prospects but with serious urban pollution and “siphoning” effects from other cities. The shrinking cities are mainly concentrated in the western, central, and northeastern regions of China, which are represented by the old industrial and resource-depleted cities. The shrinking cities in the eastern region are fewer and less severe, which is mainly related to the high population concentration and developed economy in the region. This study provides solutions from different perspectives for four types of shrinking cities and serves as an empirical reference for policymakers and urban planners.

Difference Analysis of Regional Economic Development Based on the SOM Neural Network with the Hybrid Genetic Algorithm.

Since the reform and opening up, China's regional economy has developed rapidly. However, due to different starting points of economic development caused by the traditional distribution of productive forces and the differences in regions, resources, technologies, and policies, the level of economic development in different regions is uneven. Clustering analysis is a data mining method that clusters or classifies entities according to their characteristics and then discovers the whole spatial distribution law of datasets and typical patterns. It is of great significance to classify, compare, and study the economic development level of different regions in order to formulate the regional economic development strategy. In this paper, a self-organizing feature map (SOM) neural network with the hybrid genetic algorithm is used to cluster the differences of regional economic development, the clustering results are evaluated, and the empirical results are good. From this, some meaningful conclusions can be drawn, which can provide reference for the decision-making of coordinating regional economic development.

Recognition of Fault State of RV Reducer Based on self-organizing feature map Neural Network

In order to accurately evaluate the working state of RV reducer, a fault identification method based on the fault identification model established by Self-Organizing Feature Map (SOM) Neural Network is proposed. Firstly, the data measured by the RV reducer test platform are analyzed by wavelet to obtain the wavelet coefficient. Then, combined with the efficiency data of RV reducer, the mean square frequency, center of gravity frequency and frequency variance of the two groups of data are calculated after Fourier transform and power spectrum analysis. After optimization, several eigenvalues are obtained. The eigenvalues are input into the competitive neural network and SOM neural network to establish the fault identification model. Finally, the results of the fault identification model established by the competitive neural network and SOM neural network are compared. The prediction results show that the fault identification model established by SOM neural network can effectively determine the working state of RV reducer.

Identifying Critical Area of Ecosystem Service Supply and Demand at Different Scales Based on Spatial Heterogeneity Assessment and SOFM Neural Network

The purpose of this study was to establish a spatial multi-scale integrated assessment framework for critical areas of ecosystem service supply and demand, in order to provide theoretical support for regional ecological protection planning and refined management. Taking the typical hilly area of the upper reaches of the Yangtze River in China as an example, based on the assessment matrix of land use and ecosystem services, we used the method of spatial heterogeneity assessment and self-organizing feature mapping (SOFM) to explore the identification and regionalization of critical areas of ecosystem services at regional and small scales. The results show that there was spatial heterogeneity and scale effects of ecosystem services under the two scales. The small-scale supply–demand pattern was greatly affected by microtopography and land use patterns, and the importance of ecosystem services was as follows: forest area in the upper part of the mountain > orchard and dry area in the middle and lower part of the mountain > valley farming area > flat town and farming area. The regional-scale supply–demand pattern was greatly affected by landscape structure, location conditions and social economy, and the importance of ecosystem services was as follows: south > west > north > central. The SOFM network quantitatively identified four types of ecological areas with clear dominant functions at regional and small scales. The balance between supply and demand in the studied ecosystem service areas was I < II < III < IV, in which IV was the critical area in terms of supply and I was the critical area in terms of demand. This assessment framework can improve the spatial accuracy and objectivity of the quantification and mapping of ecosystem services, and provide new ideas for multi-scale identification and expression of ecosystem services.

Spatiotemporal effects of urban sprawl on habitat quality in the Pearl River Delta from 1990 to 2018

Since the implementation of the Chinese economic reforms. The habitat quality of coastal has gradually deteriorated with economic development, but the concept of "ecological construction" has slowed the negative trend. For quantitative analysis of the correlation between the Pearl River Delta urban expansion and changes in habitat quality under the influence of the policy, we first analyzed the habitat quality change based on the InVEST model and then measured the impact of construction land expansion on the habitat quality through habitat quality change index (HQCI) and contribution index (CI) indicators. Finally, the correlation between urbanization level and habitat quality was evaluated using geographically weighted regression (GWR) and the Self-organizing feature mapping neural network (SOFM). The results indicated that: (1) during the study period from 2000 to 2020, habitat quality declined due to urban sprawl, indicating a deterioration of ecological structure and function, and the decrease was most significant from 2000 to 2010. (2) The urbanization index had a negative effect on the habitat quality, but the negative effect have improved after 2000, reflecting the positive effect of policies such as "ecological civilization construction" (3) The implementation degree of ecological civilization varies greatly among cities in the study area: Shenzhen, Dongguan, Foshan, and Zhongshan have the best level of green development. These results reflect the positive role of policies in the prevention of damage to habitat quality caused by economic development and provide a reference for the formulation of sustainable urban development policies with spatial differences.

Analysis of Enterprise Human Resources Demand Forecast Model Based on SOM Neural Network.

Human resource planning is the prerequisite of human resource management, and the basic work of human resource planning is to predict human resource demand. Scientific and reasonable human resource demand forecasting results can provide important data support for enterprise human resource planning and strategic decision-making so that human resources management can play a better role in the realization of corporate goals. Because human resource demand is affected by many factors, there is a high degree of nonlinearity and uncertainty between each factor and personnel demand, as well as the incompleteness and inaccuracy of corporate human resource data. In this paper, the self-organizing feature mapping (SOM) artificial neural network prediction model is selected as the prediction model, and the input and output process of sample data is converted into the optimal solution process of the nonlinear function. In the application of the model, the human resource demand prediction index system is used as the input of the SOM neural network and the total number of employees in the enterprise is used as the output so that the problem of nonlinear fitting between human resource demand-influencing factors and human resource demand can be solved. Finally, through the empirical analysis of the enterprise, the model forecasting process is explained and the human resource demand forecast is realized.

A Multi-Swarm ABC Algorithm for Parameters Optimization of SOFM Neural Network in Dynamic Environment

Self-organizing feature map (SOFM) neural network is a kind of competitive unsupervised learning neural network, which has strong self-organizing and self-learning capabilities. It has been widely used in the fields of data classification and data clustering. A crucial step for SOFM neural network is to set its weight parameters correctly because the output accuracy and efficiency of the network depend much on these parameters. Most of current methods for parameter setting are based on static data. However, in a dynamic environment, the statistical characteristics of the generated data will change unpredictably over time. If the SOFM network cannot react to the changes of the environment, its performance will degrade. To deal with this problem, a more powerful multi-swarm artificial bee colony algorithm (MABC) is proposed. In the algorithm, the classic ABC algorithm is improved with multi-swarm and exclusive operation strategies to make it suitable for tracking optimal parameter settings of the SOFM network, so that the SOFM network can be applied to a dynamic environment. Two real data streams, which are regarded as coming from dynamic environments, are used to evaluate the effectiveness of the algorithm. Results show that the proposed algorithm is superior to the classic SOFM algorithm in terms of clustering purity and effectiveness. It is a promising method for the classification of data streams from dynamic environments.

Coordination control of active front steering and direct yaw moment control based on stability judgment for AVs stability enhancement

A coordination control strategy based on stability judgment is presented for autonomous vehicles (AVs) aiming to enhance the handling and stability performance. Firstly, the stability judgment scheme is used to evaluate the real-time stability level of vehicles based on the Self-Organizing Feature Map (SOFM) neural network and K-Means algorithm. Secondly, a coordination controller of active front steering (AFS) and direct yaw moment control (DYC) is designed to track the desired vehicle motion. To enhance the handling and stability of AVs, the weights of AFS and DYC controllers are adaptively adjusted according to the vehicle stability level. Finally, the effectiveness of the proposed method is verified in co-simulation environment of CarSim and Simulink, and a rapid control prototyping test is implemented to evaluate the feasibility and robustness. The results indicate that the stability judgment scheme and coordination control strategy for AVs can not only satisfy the requirements of path tracking accuracy but also enhance the handling and stability performance.

Early Breast Cancer Detection Utilizing Artificial Neural Network

Breast cancer is one of the most dangerous cancer diseases for women in worldwide. A Computeraided diagnosis system is very helpful for radiologist for diagnosing micro calcification patterns earlier and faster than typical screening techniques. Maximum breast cancer cells are eventually form a lump or mass called a tumor. Moreover, some tumors are cancerous and some are not cancerous. The cancerous tumors are called malignant and non-cancerous tumors are called benign. The benign tumors are not dangerous to health. But the unchecked malignant tumors have the ability to spread in other organs of the body. For that early detection of benign and malignant tumor is important for confining the death of breast cancer. In these research study different neural networks such as, Multilayer Perceptron (MLP) Neural Network, Jordan/Elman Neural Network, Modular Neural Network (MNN), Generalized Feed-Forward Neural Network (GFFNN), Self-Organizing Feature Map (SOFM) Neural Network, Support Vector Machine (SVM) Neural Network, Probabilistic Neural Network (PNN) and Recurrent Neural Network (RNN) are used for classifying breast cancer tumor. And compare the results of these networks to find the best neural network for detecting breast cancer. The networks are tested on Wisconsin breast cancer (WBC) database. Finally, the comparing result showed that Probabilistic Neural Network shows the best detection result than other networks.

Research on Model and Algorithm of User Access Pattern Data Mining

The era of big data has come. Today, all kinds of information and data are showing explosive growth. Internet activities of different scales are struggling to catch up with the pace and pace of the development of "big data. This paper uses time series mining algorithms, and uses Microsoft's data mining tools to model the data sets collected from data halls, so as to discover the user's online behaviour patterns and potential online rules within a certain period of time. we made reasonable suggestions for the scientific management of the campus network. A new clustering method based on the improved Kohonen self-organizing feature mapping neural network is proposed. A Gaussian-shaped membership function has introduced to output several neurons with a degree of membership greater than the threshold, thereby solving the problem of mining users' multiple interests.

Intelligent Diagnosis Method of MRI Brain Image Using Parallel Self-Organizing Feature Maps Neural Network

Advances in medical imaging skills have promoted the influence of medical imaging in neuroscience. Having advanced medical imaging technology is essential for the medical industry. Magnetic resonance imaging (MRI) plays a central role in medical imaging. It plays a key role in the treatment of various human diseases. Doctors analyze brain size, shape, and location in brain MR images to assess brain disease and develop a medical plan. The manual division of brain tissue by experts is heavy and subjective. Therefore, the study of automatic segmentation of brain MR images has practical significance. Because the characteristics of brain MRI images are low contrast and high noise, which seriously affects the accuracy of image segmentation, the current image segmentation methods have some limitations in this application. In this paper, multiple self-organizing feature maps neural network (SOM-NN) are utilized to construct a parallel self-organizing feature maps neural network (PSOM-NN), which converts the segmentation problem of brain images into the classification problem of PSOMNN. The experiments show that SOM has strong self-learning ability in learning and training, and the parallel ability of PSOM-NN model greatly reduces the segmentation time, improves the real-time performance of the model, and helps to realize fully automatic or semi-automatic segmentation of the lesion area. PSOM can promote the improvement of segmentation accuracy and facilitate intelligent diagnosis.

Fusion Fault Diagnosis Model for Six-Rotor UAVs Based on Conformal Fourier Transform and Improved Self-Organizing Feature Map

If the fault sources can be located in a timely and accurate manner when an unmanned aerial vehicle (UAV) has an early minor fault, a major accident can be avoided. In light of the early fault of the six-rotor UAV motors, this paper proposes a fusion fault diagnosis model based on Conformal Fourier Transform (CFT) and the improved Self-Organizing Feature Map (SOM) neural network. Firstly, to overcome the weakening of UAV fault information caused by external interference and solve the problem that the non-uniform distribution of the collected flight data affects the accuracy of the frequency domain processing, on the basis of changing the processing method of the Fourier integral kernel and using high-order numerical integration, an improved Conformal Fourier Transform (CFT) algorithm optimized by spectrum refinement strategy is proposed. Then this algorithm is used to further intensify the main features and build a clear fault state in a cycle processing method. Secondly, an improved SOM neural network is designed. We use the unweighted average distance (UPGMA) of the agglomerative hierarchical clustering algorithm to detect the clustering areas of each pattern category of the data samples, and the weight vectors of different clustering areas are used to randomly initialize the connection weights of SOM network, thereby improving the clustering speed and convergence effect. On this basis, CFT algorithm is integrated into the improved SOM network to realize the multi-module collaboration strategy and finally form a new fusion fault diagnosis model.

Research on Indoor Wireless Positioning Precision Optimization Based on UWB

The ultra-wide band (UWB) indoor positioning precision often has large deviations due to environmental influences. To reduce noise error and improve the UWB indoor positioning precision, this paper divides the indoor positioning into static positioning and mobile positioning, and proposes different optimization algorithms for the two positioning modes. An improved self-organizing feature mapping neural network clustering algorithm is used for static positioning. After training, the layout of the neural network is established, and each weight vector is located at the center of the input vector cluster. Experiments indicate that the fully trained neural network can effectively filter noise, and reduce the mean square error significantly within 3.0×10−3. The positioning precision is 32.39% and 17.24% higher than those of the K-mean filtering algorithm and the Kalman filtering algorithm. For mobile positioning, the optimized neural network clustering algorithm is integrated with the unscented Kalman filter (UKF) to smooth the positioning data and reduce non-line-of-sight (NLOS) error. Experiments prove that this method can effectively reduce the errors caused by the NLOS state change, and estimate the distance with a high precision for ultra-wide band positioning and tracking.