Self-organizing Neural Network Model Research Articles

Unveiling the evolution of urban rail transit network: considering ridership attributes

ABSTRACT Previous research on urban rail transit (URT) evolution mainly focused on network topology, neglecting ridership attributes. This study extracts ridership and network topology indicators from Chinese URT data. Employing a self-organizing mapping neural network model, it divides China’s URT development into four stages. The initial stage and the development stage form the framework of URT network. The network diameter reaches the maximum in the networked operation stage. In the mature stage, URT network densification occurs alongside a significant increase in resident ridership. It is also found that each network indicator has a significant nonlinear relationship with ridership attributes. These findings are of guiding significance for urban planners to accurately understanding URT’s future development and rational network planning and construction.

Design and prediction of self-organizing interval type-2 fuzzy wavelet neural network

This paper presents a self-organizing interval type-2 fuzzy wavelet neural network (SIT2FWNN) model for predicting and identifying nonlinear systems. Based on traditional TSK interval type-2 fuzzy neural network (IT2FNN), the proposed SIT2FWNN utilizes the wavelet basis function with the locating abilities of time-domain and frequency-domain as the consequent of fuzzy rules, combining the capacity of IT2FNN to handle the uncertainty and the great learning potentiality of wavelet neural network (WNN). For the structural adjustment of SIT2FWNN, fuzzy rules are added and deleted according to the Euclidean distance between the input layer and the fuzzification layer. The self-organizing algorithm can delete redundant and unimportant fuzzy rules, thus optimizing the structure of SIT2FWNN and simplifying the calculation. In parameter learning, the AdaBound algorithm and self-adaptive gradient learning algorithm are used to find optimal values of unknown parameters of the SIT2FWNN model. Finally, the designed SIT2FWNN model is applied in the predictions of short-term traffic flow, Mackey-Glass time series, and the opening index of the Shanghai stock index. The evaluation comparison between the proposed model and similar studies proves that SIT2FWNN has higher prediction accuracy and speed.

Modeling and Energy-Optimal Control for Freight Trains based on Data-Driven Approaches

This paper focuses on the energy optimization problem of traction substations. The paper addresses the difficulty of considering time-varying parameters and environmental characteristics of freight train in mechanism modeling. In response, a new optimization method called “Modeling and Energy-Optimal Control for Freight Trains based on Data-Driven Approaches” is proposed. Firstly, a data-driven model considering the “Train-Track-Power grid” (TTP) is constructed based on recorded data. The energy optimization problem is regarded as a finite Markov decision process. A dynamic programming (DP) algorithm is utilized to optimize the train’s control force. The data-driven model is then used to solve for the train’s speed curve, traction substation power, and contact network voltage. Secondly, a multi-input multi-output self-organizing fuzzy neural network (MIMO-SOFNN) model is designed during the modeling process, and an Levenberg–Marquardt algorithm with self-adaption damping factor (SA-LM) is proposed for model parameter learning. Finally, experimental analysis is conducted to validate the high accuracy of the MIMO-SOFNN model compared to five other models. The effectiveness of the SA-LM algorithm is also verified through a comparison with five other algorithms. In the energy optimization experiments, when compared with the actual operation data of a freight railway company in China, the proposed energy optimization method in this paper reduces the energy consumption of the traction substation by 34.8%.

Intelligent upgrade of waste-activated sludge dewatering process based on artificial neural network model: Core influential factor identification and non-experimental prediction of sludge dewatering performance

Owing to the extremely complex compositions and origins of waste-activated sludge (WAS), the multiple physiochemical properties of WAS have impacts on its dewaterability, and there is a complex interaction relationship among the multiple physiochemical properties, which makes it difficult to identify the controlling factors on WAS dewaterability. Accordingly, there is still no unified certainty in the appropriate ranges of physiochemical properties for the optimal dewaterability of sludge from different sources, resulting in a lack of clear theoretical basis for technical selection and optimization of sludge dewatering processes. The large consumption of conditioning chemicals and low process efficiency stand for the major deficiency of existing sludge conditioning technologies. This study proposed to use a non-linear, adaptive and self-organizing artificial neural network (ANN) model to integrate the multiple physiochemical properties of WAS affecting its dewaterability, and WAS dewatering performance under certain conditioning schemes could be predicated by ANN model with the multiple physiochemical properties and conditioning operation parameters as the input arguments. Thus, the laborious filtration experiments for screening conditioning chemicals could be replaced by the input adjustment of ANN model. Rooted mean squared error (RMSE) of 6.51 and coefficient of determination (R2) of 0.73 confirmed the satisfied stability and accuracy of established ANN model. Furthermore, the predictor-exclusive method revealed that the exclusion of polar interface free energy decreased most, which reflected the importance of surface hydrophilicity reduction in sludge dewaterability improvement. All the contributions presented here were believed to provide an intelligent insight to improve the experience operation status of WAS dewatering process.

Online Monitoring of Ecoenvironmental Pollution Data in Internet Environment

In view of the problems of poor air quality and serious soil pollution in the current ecological environment, an online monitoring of ecological environment pollution data in the Internet environment is proposed. The concentration of pollution sources that may appear in the atmospheric environment is analyzed through the analysis coefficient of ecological environment pollutants, and the specific grade data corresponding to the air quality index is determined. Observation and mapping are carried out for the area to be monitored, and then, the monitoring points are determined, the monitoring equipment is arranged, and the soil profile of the area is sampled in the monitoring area. The self-organizing neural network model is used to conduct spatial cluster analysis on the pollution of ecological environment pollution areas, evaluate the sensitivity of soil pollution, and build a land ecological pollution monitoring information model. Based on the Internet of Things technology and the comprehensive use of GPS, sensors, and other technologies and equipment, the framework of monitoring technology is constructed, and the online monitoring of ecological environment pollution data is completed. The experimental results show that the proposed method has high data accuracy in different locations and different seasons, which is consistent with the actual situation; because of the maturity of the Internet of Things technology, compared with the literature method, the method in this paper can provide a cost-effective data foundation, which is more cost-effective, and save time and energy.

The Role of Idiothetic Signals, Landmarks, and Conjunctive Representations in the Development of Place and Head-Direction Cells: A Self-Organizing Neural Network Model.

Place and head-direction (HD) cells are fundamental to maintaining accurate representations of location and heading in the mammalian brain across sensory conditions, and are thought to underlie path integration—the ability to maintain an accurate representation of location and heading during motion in the dark. Substantial evidence suggests that both populations of spatial cells function as attractor networks, but their developmental mechanisms are poorly understood. We present simulations of a fully self-organizing attractor network model of this process using well-established neural mechanisms. We show that the differential development of the two cell types can be explained by their different idiothetic inputs, even given identical visual signals: HD cells develop when the population receives angular head velocity input, whereas place cells develop when the idiothetic input encodes planar velocity. Our model explains the functional importance of conjunctive “state-action” cells, implying that signal propagation delays and a competitive learning mechanism are crucial for successful development. Consequently, we explain how insufficiently rich environments result in pathology: place cell development requires proximal landmarks; conversely, HD cells require distal landmarks. Finally, our results suggest that both networks are instantiations of general mechanisms, and we describe their implications for the neurobiology of spatial processing.

An improved self-organizing incremental neural network model for short-term time-series load prediction

Load prediction is a crucial component for optimal building energy management. The challenge with buildings’ load prediction is the lack of historical data since not all sites have a large amount of collected data. One of the solutions is incremental learning that updates the trained model with the most recent data. It allows the model to be deployed as soon as possible and improving its prediction accuracy as time progress. This study studies the effect of incremental learning. This study also proposes a novel DB-SOINN-R model that is based on the enhanced self-organizing incremental neural network (ESOINN), which is one of the incremental learning models. The problems with ESOINN are the inappropriate node removal by the original denoising of ESOINN, the inappropriate Euclidean distance for training data with imbalanced dimensions that usually consist of less discrete timestamp data compared to time-series historical data, and the incapability to obtain unique predicted outputs. To tackle these problems, the DB-SOINN-R incorporates a new density-based denoising that replaces the original denoising, a new mean Euclidean distance as the distance metric to handle training data with imbalanced dimensions, and k-nearest-neighbor inverse distance weighting (kNN-IDW) regression to obtain unique predicted output for every different input. The proposed DB-SOINN-R is compared with five models: feedforward neural network, deep neural network with long-short-term memory, support vector regression, ESOINN, and kNN regression. They are tested on day-ahead and one-hour-ahead load predictions, using two different datasets. The proposed DB-SOINN-R has the highest prediction accuracy among all models with incremental learning in both datasets.

Evaluation of fitness state of sports training based on self-organizing neural network

Self-organization theory has become a hotspot in system engineering scientific theoretical research. However, there are few studies on autonomous adaptation to sports training from the perspective of self-organization theory, and the application of self-organizing neural network models in sports training is relatively rare. Guided by multidisciplinary knowledge and various scientific principles, this article uses modern scientific and technological achievements and adopts scientific training methods and means to implement optimal control over the entire process of sports training. Moreover, this article uses a self-organization theory that has the characteristics of new things and has been successfully used in other fields and neural network models to study sports training adaptation theory. The evaluation model of athletes' training adaptation status established by a self-organizing competitive neural network designed by self-organization principle also proves the feasibility of applying self-organization theory to sports training adaptation.

Shannon Information Entropy for Seismic Reliability Assessment of Structures Using Self-Organizing Neural Networks

Localization and quantification of structural damages and find a failure probability is the key important in reliability assessment of structures. In this study, a Self-Organizing Neural Network (SONN) with Shannon Information Entropy simulation is used to reduce the computational effort required for reliability analysis and damage detection. To this end, one demonstrative structure is modeled and then several damage scenarios are defined. These scenarios are considered as training datasets for establishing a Self-Organizing Neural Network model. In this regard, the relation between structural responses (input) and structural stiffness (output) is established using Self-Organizing Neural Network models. The established SONN is more economical and achieves reasonable accuracy in detection of structural damages under ground motion. Furthermore, in order to assess the reliability of structure, five random variables are considered. Namely, columns’ area of first, second and third floor, elasticity modulus and gravity loads. The SONN is trained by Shannon Information Entropy simulation technique. Finally, the trained neural network specifies the failure probability of purposed structure. Although MCS can predict the failure probability for a given structure, the SONN model helps simulation techniques to receive an acceptable accuracy and reduce computational effort.

Learning ADL Daily Routines with Spatiotemporal Neural Networks

Activities of daily living (ADLs) refer to the activities performed by individuals on a daily basis and are the indicators of a person's habits, lifestyle, and wellbeing. Consequently, learning an individual's ADL daily routines has significant value in the healthcare domain. Specifically, ADL recognition and inter-ADL pattern learning problems have been studied extensively in the past couple of decades. However, discovering the patterns of ADLs performed in a day and clustering them into ADL daily routines has been a relatively unexplored research area. In this paper, a self-organizing neural network model, called the Spatiotemporal ADL Adaptive Resonance Theory (STADLART), is proposed for learning ADL daily routines. STADLART integrates multimodal contextual information that involves the time and space wherein the ADLs are performed. By encoding spatiotemporal information explicitly as input features, STADLART enables the learning of time-sensitive knowledge. Moreover, a STADLART variation named STADLART-NC is proposed to normalize and customize ADL weighting for daily routine learning. A weighting assignment scheme is presented that facilitates the assignment of weighting according to ADL importance in specific domains. Empirical experiments using both synthetic and real-world public data sets validate the performance of STADLART and STADLART-NC when compared with alternative pattern discovery methods. The results show that STADLART could cluster ADL routines with better performance than baseline algorithms.

A Modeling Study of the Emergence of Eye Position Gain Fields Modulating the Responses of Visual Neurons in the Brain.

The responses of many cortical neurons to visual stimuli are modulated by the position of the eye. This form of gain modulation by eye position does not change the retinotopic selectivity of the responses, but only changes the amplitude of the responses. Particularly in the case of cortical responses, this form of eye position gain modulation has been observed to be multiplicative. Multiplicative gain modulated responses are crucial to encode information that is relevant to high-level visual functions, such as stable spatial awareness, eye movement planning, visual-motor behaviors, and coordinate transformation. Here we first present a hardwired model of different functional forms of gain modulation, including peaked and monotonic modulation by eye position. We use a biologically realistic Gaussian function to model the influence of the position of the eye on the internal activation of visual neurons. Next we show how different functional forms of gain modulation by eye position may develop in a self-organizing neural network model of visual neurons. A further contribution of our work is the investigation of the influence of the width of the eye position tuning curve on the development of a variety of forms of eye position gain modulation. Our simulation results show how the width of the eye position tuning curve affects the development of different forms of gain modulation of visual responses by the position of the eye.

A Novel Self-Organizing Fuzzy Neural Network to Learn and Mimic Habitual Sequential Tasks.

In this article, a new self-organizing fuzzy neural network (FNN) model is presented which is able to simultaneously and accurately learn and reproduce different sequences. Multiple sequence learning is important in performing habitual and skillful tasks, such as writing, signing signatures, and playing piano. Generally, it is indispensable for pattern generation applications. Since multiple sequences have similar parts, local information such as some previous samples is not sufficient to efficiently reproduce them. Instead, it is necessary to consider global and discriminative information, maybe in the very initial samples of each sequence, to first recognize them, and then predict their next sample based on the current local information. Therefore, the structure of the proposed network consists of two parts: 1) sequence identifier, which computes a novel sequence identity value based on initial samples of a sequence, and detects the sequence identity based on proper fuzzy rules and 2) sequence locator, which locates the input sample in the sequence. Therefore, by integrating outputs of these two parts in fuzzy rules, the network is able to produce the proper output based on the current state of each sequence. To learn the proposed structure, a gradual learning procedure is proposed. First, learning is performed by adding new fuzzy rules, based on coverage measure, using available correct data. Next, the initialized parameters are fine-tuned, by the gradient descent algorithm, based on fed back approximated network output as the next input. The proposed method has a dynamic structure able to learn new sequences online. Finally, to investigate the effectiveness of the presented approach, it is used to simultaneously learn and reproduce multiple sequences in different applications, including sequences with similar parts, different patterns, and writing different letters. The performance of the proposed method is evaluated and compared with other existing methods, including the adaptive network-based fuzzy inference system, GDFNN, CFNN, and long short-term memory (LSTM). According to these experiments, the proposed method outperforms traditional FNNs and LSTM in learning multiple sequences.

A rainwater control optimization design approach for airports based on a self-organizing feature map neural network model

To address the problems of high overflow rate of pipe network inspection well and low drainage efficiency, a rainwater control optimization design approach based on a self-organizing feature map neural network model (SOFM) was proposed in this paper. These problems are caused by low precision parameter design in various rainwater control measures such as the diameter of the rainwater pipe network and the green roof area ratio. This system is to be combined with the newly built rainwater pipe control optimization design project of China International Airport in Daxing District of Beijing, China. Through the optimization adjustment of the pipe network parameters such as the diameter of the rainwater pipe network, the slope of the pipeline, and the green infrastructure (GI) parameters such as the sinking green area and the green roof area, reasonable control of airport rainfall and the construction of sustainable drainage systems can be achieved. This research indicates that compared with the result of the drainage design under the initial value of the parameter, the green roof model and the conceptual model of the mesoscale sustainable drainage system, in the case of a hundred-year torrential rainstorm, the overflow rate of pipe network inspection wells has reduced by 36% to 67.5%, the efficiency of drainage has increased by 26.3% to 61.7%, which achieves the requirements for reasonable control of airport rainwater and building a sponge airport and a sustainable drainage system.

A novel boot strapping algorithm for text extraction in a self-organising neural network model

With rapid growth in internet and its associated communication protocols, need for printed documents to be carried over from one place to another has been reduced to minimise the cost and time. Research contributions in the past have paved the way for implementation of smart and intelligent algorithms to further minimise manual intervention in processing of documents. One such area is the automation of text extraction from documents with increased accuracy and least number of false detections. A wide range of algorithms and methodologies have been reported in the past towards efficient extraction of text from documents which may be online or offline. This research paper proposes an efficient extraction algorithm of text from given set of documents which may or may not be graphic through utilisation of a hybrid SOM-ANN algorithm. The experimentation has been done over a wide variety of inputs and convergence of error in extraction is found to be minimum when compared to other conventional extractors.

Self-organising coordinate transformation with peaked and monotonic gain modulation in the primate dorsal visual pathway.

We study a self-organising neural network model of how visual representations in the primate dorsal visual pathway are transformed from an eye-centred to head-centred frame of reference. The model has previously been shown to robustly develop head-centred output neurons with a standard trace learning rule, but only under limited conditions. Specifically it fails when incorporating visual input neurons with monotonic gain modulation by eye-position. Since eye-centred neurons with monotonic gain modulation are so common in the dorsal visual pathway, it is an important challenge to show how efferent synaptic connections from these neurons may self-organise to produce head-centred responses in a subpopulation of postsynaptic neurons. We show for the first time how a variety of modified, yet still biologically plausible, versions of the standard trace learning rule enable the model to perform a coordinate transformation from eye-centred to head-centred reference frames when the visual input neurons have monotonic gain modulation by eye-position.

Identification of Motion Conditions Based on Self-organizing Competitive Neural Network Algorithm

Against the false alarms and false negatives caused by the error of alarm threshold selection because different working conditions when the early warning device is close to the same charged body, this paper proposes self-organizing competitive neural networ modle for Identification working conditions of worker, which climbing towers, climbing slopes and horizontal walking. Firstly, acceleration sensors and barometric pressure sensors are used to collect the acceleration value and barometric data of the head during the exercise of the experimenter. Secondly, Multi-source information collaborative filtering processes data to obtain effective relative height values and obtain fitting parameters by first-order fitting. Finally, building self-organizing competitive neural network model based on parameters. This paper selects outdoor towers, slopes with a slope of about 30° and horizontal roads as experimental platforms and collect 400 sets of data for each platform. Then randomly select 900 sets of data for training, 300 sets of data for verification. The experimental results show that the accuracy of the training sample reaches 94.67%, and the accuracy of the test sample reaches 92.73%, which meets the requirements for working condition identification in the outdoor environment.

Design a management information system for financial risk control

This paper presents an evaluation of the financial system risk quantitatively and a management information system designed to improve financial risk management. Based on the recognition of risk sources for all kinds of financial institutions and financial markets, we use a matrix covering financial risk probability and related damage to assess local risks and carry out risk classification, and use self-organizing mapping neural network model to evaluate the overall risks. Also, by means of radio frequency identification technology and big data analytics platform—Hadoop platform, a management information system is built which could perform three core functions: real-time monitor , analysis and evaluation, and automatic control, which would help regulators to realize the whole process and comprehensive intelligent management for financial risk sources.

A model of human motor sequence learning explains facilitation and interference effects based on spike-timing dependent plasticity

The ability to learn sequential behaviors is a fundamental property of our brains. Yet a long stream of studies including recent experiments investigating motor sequence learning in adult human subjects have produced a number of puzzling and seemingly contradictory results. In particular, when subjects have to learn multiple action sequences, learning is sometimes impaired by proactive and retroactive interference effects. In other situations, however, learning is accelerated as reflected in facilitation and transfer effects. At present it is unclear what the underlying neural mechanism are that give rise to these diverse findings. Here we show that a recently developed recurrent neural network model readily reproduces this diverse set of findings. The self-organizing recurrent neural network (SORN) model is a network of recurrently connected threshold units that combines a simplified form of spike-timing dependent plasticity (STDP) with homeostatic plasticity mechanisms ensuring network stability, namely intrinsic plasticity (IP) and synaptic normalization (SN). When trained on sequence learning tasks modeled after recent experiments we find that it reproduces the full range of interference, facilitation, and transfer effects. We show how these effects are rooted in the network’s changing internal representation of the different sequences across learning and how they depend on an interaction of training schedule and task similarity. Furthermore, since learning in the model is based on fundamental neuronal plasticity mechanisms, the model reveals how these plasticity mechanisms are ultimately responsible for the network’s sequence learning abilities. In particular, we find that all three plasticity mechanisms are essential for the network to learn effective internal models of the different training sequences. This ability to form effective internal models is also the basis for the observed interference and facilitation effects. This suggests that STDP, IP, and SN may be the driving forces behind our ability to learn complex action sequences.

Investigating the key factors for socially excluded groups in college

This paper aims to identify the key factors that are essential to the behaviour and life of socially excluded groups in colleges. We first conduct a questionnaire filled by undergraduate students from different colleges in China. Then the data collected by the questionnaire was studied by descriptive analysis, chi-square test and correlation analysis on the relationships between the seven key factors and three aspects. For better understanding, the distribution of socially excluded groups, we propose a self-organising artificial neural network model to cluster and analyse the proportion of socially excluded group and non-socially excluded group based on the seven factors. We find that the rate of social exclusion in college is about 18%. This paper offers an analytical method to determine whether a student is socially excluded or not in college by measuring the key factors of the social exclusion of campus life.

Where's the Noise? Key Features of Spontaneous Activity and Neural Variability Arise through Learning in a Deterministic Network.

Even in the absence of sensory stimulation the brain is spontaneously active. This background “noise” seems to be the dominant cause of the notoriously high trial-to-trial variability of neural recordings. Recent experimental observations have extended our knowledge of trial-to-trial variability and spontaneous activity in several directions: 1. Trial-to-trial variability systematically decreases following the onset of a sensory stimulus or the start of a motor act. 2. Spontaneous activity states in sensory cortex outline the region of evoked sensory responses. 3. Across development, spontaneous activity aligns itself with typical evoked activity patterns. 4. The spontaneous brain activity prior to the presentation of an ambiguous stimulus predicts how the stimulus will be interpreted. At present it is unclear how these observations relate to each other and how they arise in cortical circuits. Here we demonstrate that all of these phenomena can be accounted for by a deterministic self-organizing recurrent neural network model (SORN), which learns a predictive model of its sensory environment. The SORN comprises recurrently coupled populations of excitatory and inhibitory threshold units and learns via a combination of spike-timing dependent plasticity (STDP) and homeostatic plasticity mechanisms. Similar to balanced network architectures, units in the network show irregular activity and variable responses to inputs. Additionally, however, the SORN exhibits sequence learning abilities matching recent findings from visual cortex and the network’s spontaneous activity reproduces the experimental findings mentioned above. Intriguingly, the network’s behaviour is reminiscent of sampling-based probabilistic inference, suggesting that correlates of sampling-based inference can develop from the interaction of STDP and homeostasis in deterministic networks. We conclude that key observations on spontaneous brain activity and the variability of neural responses can be accounted for by a simple deterministic recurrent neural network which learns a predictive model of its sensory environment via a combination of generic neural plasticity mechanisms.