Risk assessment of knowledge fusion in an innovation ecosystem based on a GA-BP neural network

The risk assessment of knowledge fusion in innovation ecosystems is directly related to these ecosystems’ success or failure. A back-propagation (BP) neural network optimized by a genetic algorithm (GA) is thus proposed to evaluate the risk of knowledge fusion in innovation ecosystems. First, an index system is constructed for evaluating the risk of knowledge fusion in innovation ecosystems, and data are collected by questionnaire for use as training data for the neural networks. To realize machine learning, 84 datasets were generated, of which 60 were used to train the network, and 24 were used to test the network in MATLAB (R2014b). Evaluation models were then constructed by the BP neural network and GA-BP neural network, and their accuracy was judged by comparing the evaluation value with the target value. The comparison shows that the GA-BP neural network has faster convergence speed and higher stability, can achieve the goal more often, and reduces the possibility of the BP neural network falling into a local optimum instead of reaching global optimization. The GA-BP neural network model for the knowledge fusion risk assessment of innovation ecosystems provides a new method for practice.     

Analysis of regional environment and regional innovation strategy based on cybernetics

In this paper, based on the perspective of carbon emissions, the regional environment and regional innovation strategies were analyzed by using cybernetics. The main idea was to calibrate the diesel engine simulation model with predictive function based on the data collected from the bench test, and then according to the simulation model, the genetic algorithm was applied to optimize the EGR parameters and fuel injection parameters with the target of emission generation and output torque. First of all, the combustion process and emission performance of diesel engine with internal exhaust re-circulation were studied by genetic algorithm, and the genetic algorithm model was constructed. Then, the genetic algorithm program was written based on Matlab. In addition, taking the NOx emission as the target, and the Soot emission and the output torque of the original machine as constraints, through the joint simulation of Matlab and GT-Power, the EGR parameters and fuel injection parameters of the 10% to 50% load conditions were optimized at the speed of 1500 r/min and 2000 r/min. Finally, the validity of this method was verified and analyzed.     

Application of improved BP neural network based on e-commerce supply chain network data in the forecast of aquatic product export volume

Aiming at the existing problems in the production and export scale prediction of aquaculture, a model of yield prediction based on BP Neural network algorithm is proposed, and a set of algorithms is proposed to optimize BP neural network (BPNN). Based on the traditional BP neural network, it is easy to get into the local optimal problem due to the long training time of the model. By using the simple Johnson algorithm, the dimensionality of the input neuron is reduced, and then the hidden layer neural network is determined by this method. At the same time, the data mining method is used to filter the Data.Particle swarm optimization algorithm is used to optimize the parameters. At the same time, based on the domestic e-commerce Sales network data, the results show that the average square root error of the model is less than the traditional BP neural network and the learning efficiency is higher than the traditional BP neural network. The results show that the model has a great advantage in building up a large number of historical data, and it can shorten the modeling time and get good prediction result by combining the sales data of e-commerce. It provides a new feasible method for the export prediction of aquatic products.     

面孔加工的认知神经科学研究：回顾与展望

面孔加工的认知神经科学研究中的核心问题是,是否存在功能和神经机制上独立的面孔加工模块以及面孔加工系统的组织形式。使用电生理、脑成像以及对脑损伤病人进行神经心理学检查等手段,研究者已经找到选择性地对面孔反应的脑区,即梭状回面孔区（FFA）。文章从面孔加工系统的特异性与多成分性以及面孔识别模型等方面,系统回顾了该领域的主要研究成果。文章最后还简单展望了今后的研究方向。     

Enterprise credit risk evaluation based on neural network algorithm

To explore the enterprise credit risk evaluation, the application effect of several common neural network models in Chinese small and medium-sized enterprise data sets was compared and the optimal parameters for each model were determined. In addition, the classification accuracy and the applicability of the model were compared, and finally the common problem of optimization neural network algorithm based on population was solved: need to determine the dimensions in advance. The experimental results showed that the probabilistic neural network (PNN) had the minimum error rate and second types of errors, while the PNN model had the highest AUC value and was robust. To sum up, the algorithm makes some contributions to solve the financing problem of small and medium-sized enterprises in China.     

粗糙集和神经网络在心理测量中的应用

探讨当因素分析和多元回归方法的使用条件未得到满足时,是否可采用粗糙集方法进行观察变量的精简,以及是否可采用神经网络方法进行预测效度检验。理论分析了粗糙集和神经网络在心理测量中应用的可能性,并运用粗糙集对于人事干部胜任力评估数据进行分析,比较了7种离散化方法和2种约简算法构成的14种组合,发现当采用Manual方法进行离散化、遗传算法进行约简时,能够很好地对观测变量进行精简;运用概率神经网络能够比等级回归方法更好地进行预测效度检验。研究结果表明对于处理心理测量中的非等距变量,粗糙集和神经网络是非常有用的方法     

Multi-layer architecture for adaptive fuzzy inference system with a large number of input features

The Sugeno adaptive fuzzy neural network using training data is a good approximation to model different systems. The large number of adaptive neuro-fuzzy inference system (ANFIS) input features is a major challenge in using ANFIS and is not applicable with increased parameters. We present a solution for many input features solving modular problems; we created a multi-layer architecture of SUB-ANFIS (MLA-ANFIS) for this purpose. Different topologies were created with various combinations of multiple input features, and an error indicator was calculated for each combination of topologies. Finally, the best topology was chosen among the states with the highest possible performance. We implemented a multi-layered approach based on 365-day concrete compressive strength data with eight input features and the optimized MLA-ANFIS topology (5-3-1) for this purpose from different ANFIS topologies and neural networks. Finally, the results from five other datasets prove the impact of the proposed MLA-ANFIS approach compared to the neural network method.     

Rule extraction using ensemble of neural network ensembles

Neural networks are well-known for their impressive classification performance, and the ensemble learning technique acts as a catalyst to improve this performance even further by integrating multiple networks.However, neural network ensembles, like neural networks, are regarded as a black box because they cannot explain their decision-making process. As a result, despite their high classification performance, neural networks and their ensembles are unsuitable for some applications that require explainable decisions. However, the rule extraction technique can overcome this drawback by representing the knowledge learned by a neural network in the guise of interpretable decision rules. A rule extraction algorithm provides neural networks the ability to justify their classification responses using explainable classification rules. There are several rule extraction algorithms for extracting classification rules from neural networks, but only a few of them use neural network ensembles to generate rules. As a result, this paper proposes a rule extraction algorithm called Rule Extraction Using Ensemble of Neural Network Ensembles (RE-E-NNES) to demonstrate the high performance of neural network ensembles.RE-E-NNES extracts classification rules by ensembling several neural network ensembles. The results demonstrate the efficacy of the proposed RE-E-NNES algorithm in comparison to other existing rule extraction algorithms.     

Emotional Valence Precedes Semantic Maturation of Words: A Longitudinal Computational Study of Early Verbal Emotional Anchoring

We present a longitudinal computational study on the connection between emotional and amodal word representations from a developmental perspective. In this study, children's and adult word representations were generated using the latent semantic analysis (LSA) vector space model and Word Maturity methodology. Some children's word representations were used to set a mapping function between amodal and emotional word representations with a neural network model using ratings from 9-year-old children. The neural network was trained and validated in the child semantic space. Then, the resulting neural network was tested with adult word representations using ratings from an adult data set. Samples of 1210 and 5315 words were used in the child and the adult semantic spaces, respectively. Results suggested that the emotional valence of words can be predicted from amodal vector representations even at the child stage, and accurate emotional propagation was found in the adult word vector representations. In this way, different propagative processes were observed in the adult semantic space. These findings highlight a potential mechanism for early verbal emotional anchoring. Moreover, different multiple linear regression and mixed-effect models revealed moderation effects for the performance of the longitudinal computational model. First, words with early maturation and subsequent semantic definition promoted emotional propagation. Second, an interaction effect between age of acquisition and abstractness was found to explain model performance. The theoretical and methodological implications are discussed.     

人机交互过程中认知负荷的综合测评方法

设计模拟网络引擎搜索和心算双任务实验,分析主观评定、绩效测量和生理测量三类评估指标对认知负荷变化的敏感性;采用因素分析、BP神经网络和自组织神经网络三种建模方法,探索人机交互过程中认知负荷的综合评估建模方法。结果显示：心理努力、任务主观难度、注视时间、注视次数、主任务反应时、主任务正确率6个指标对认知负荷变化敏感;采用多维综合评估模型对双任务作业认知负荷进行测量总体上比采用单一评估指标的测量更为有效。BP网络和自组织神经网络两种神经网络模型对认知负荷的测量结果优于传统的因素分析方法     

智力运动专家领域内知觉与记忆的加工特点及其机制

智力运动是以开发智力为目的且涉及到较多认知活动的竞技运动。研究表明, 长期的智力运动经验会影响专家在领域内任务中知觉及记忆的行为表现及其大脑活动。智力运动经验使专家知觉广度增大的同时, 促进专家对棋子关系进行整体性知觉加工, 且这一过程与颞顶联合区、缘上回、压后皮质、侧副沟、梭状回等区域有关; 在长时记忆中存储的具体(空间位置)及抽象信息(知识、策略、棋子关系等)是专家记忆优势发生的基础, 该过程与内侧颞叶、额叶和顶叶有关。未来研究可以从智力运动类型、创新实验范式, 结合测量设备及认知特点, 深入探讨智力运动专家整体知觉优势及记忆优势的神经机制, 为人工智能和技能训练等提供理论依据。     

Feature extraction of event-related potentials using wavelets: an application to human performance monitoring

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many free parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance.     

Using spotted hyena optimizer for training feedforward neural networks

Spotted hyena optimizer (SHO) is a novel metaheuristic optimization algorithm based on the behavior of spotted hyena and their collaborative behavior in nature. In this paper, we design a spotted hyena optimizer for training feedforward neural network (FNN), which is regarded as a challenging task since it is easy to fall into local optima. Our objective is to apply metaheuristic optimization algorithm to tackle this problem better than the mathematical and deterministic methods. In order to confirm that using SHO to train FNN is more effective, five classification datasets and three function-approximations are applied to benchmark the performance of the proposed method. The experimental results show that the proposed SHO algorithm for optimization FNN has the best comprehensive performance and has more outstanding performance than other the state-of-the-art metaheuristic algorithms in terms of the performance measures.     

Modeling the categorical perception of speech sounds: A step toward biological plausibility

Our native language has a lifelong effect on how we perceive speech sounds. Behaviorally, this is manifested as categorical perception, but the neural mechanisms underlying this phenomenon are still unknown. Here, we constructed a computational model of categorical perception, following principles consistent with infant speech learning. A self-organizing network was exposed to a statistical distribution of speech input presented as neural activity patterns of the auditory periphery, resembling the way sound arrives to the human brain. In the resulting neural map, categorical perception emerges from most single neurons of the model being maximally activated by prototypical speech sounds, while the largest variability in activity is produced at category boundaries. Consequently, regions in the vicinity of prototypes become perceptually compressed, and regions at category boundaries become expanded. Thus, the present study offers a unifying framework for explaining the neural basis of the warping of perceptual space associated with categorical perception.     

Computational models of inductive reasoning using a statistical analysis of a Japanese corpus

Existing computational models of human inductive reasoning have been constructed based on psychological evaluations concerning the similarities or relationships between entities. However, the costs involved in collecting psychological evaluations for the sheer number of entities that exist mean that they are prohibitively impractical. In order to avoid this problem, the present article examines three types of models: a category-based neural network model, a category-based Bayesian model, and a feature-based neural network model. These models utilize the results of a statistical analysis of a Japanese corpus computing co-occurrence probabilities for word pairs, rather than using psychological evaluations. Argument strength ratings collected by a psychological experiment were found to correlate well with simulations for the category-based neural network model.     

Index tracking based on deep neural network

Deep learning has a strong ability to extract feature representations from data, since it has a great advantage in processing nonlinear and non-stationary data and reflecting nonlinear interactive relationship. This paper proposes to apply deep learning algorithms including deep neural network and deep autoencoder to track index performance and introduces a dynamic weight calculation method to measure the direct effects of the stocks on index. The empirical study takes historical data of Hang Seng Index (HSI) and its constituents to analyze the effectiveness and practicability of the index tracking method. The results show that the index tracking method based on deep neural network has a smaller tracking error, and thus can effectively track the index.     

Identification of biological neurons using adaptive observers

This paper is to investigate the use of adaptive observers for the modeling of biological neurons and networks. Assuming that a neuron can be modeled as a continuous-time nonlinear system, it is possible to determine its unknown parameters using adaptive observer, based on the concept of adaptive synchronization. The same technique can be extended for the identification of an entire biological neural network. Some conventional observer designs are studied in this paper and satisfactory results are obtained, yet with some restrictions. To further extend the applicability of adaptive observers for the modeling process, a new design is suggested. It is based on a combination of linear feedback control approach and the dynamical minimization algorithm. The effectiveness of the designed adaptive observer is confirmed with simulations.     

Research and application of virtual user context information security strategy based on group intelligent computing

This article first introduced the current technology of the privacy protection model, and analyzed their characteristics and deficiencies. Afterwards, from the point of view of revenue, the shortcomings of the traditional privacy protection model have analyzed through the group intelligent computing method. Based on this, this paper proposes a research and application of virtual user information of security strategy based on group intelligent computing, through the collection of visitor's private information historical access data, intelligent calculation of the strategy group between the visitor and the interviewee. The setting of the threshold of the visited person can protect the privacy information of the user more effectively. In this paper, the implementation flow, algorithm implementation process, and specific architecture design of the proposed virtual user of privacy protection model based on group intelligent computing are introduced respectively. The specific algorithms include PCA, BP neural network, and genetic algorithm. Finally, the proposed privacy has verified through experiments. Protection model can protect user privacy more effectively than traditional privacy protection model. In the future, we will further expand and improve the privacy protection model of virtual users based on group intelligent computing, including considering the dynamic and inconsistency of access to the privacy information, that is, accessing different private information will produce different overlay effects and parallelism. We will also study how to apply this model to actual systems such as shopping websites and social platforms, and use commercial data to evaluate the performance of the model and further improve it.     

From embodying tool to embodying alien limb: sensory-motor modulation of personal and extrapersonal space

Years ago, it was demonstrated (e.g., Rizzolatti et al. in Handbook of neuropsychology, Elsevier Science, Amsterdam, 2000) that the brain does not encode the space around us in a homogeneous way, but through neural circuits that map the space relative to the distance that objects of interest have from the body. In monkeys, relatively discrete neural systems, characterized by neurons with specific neurophysiological responses, seem to be dedicated either to represent the space that can be reached by the hand (near/peripersonal space) or to the distant space (far/extrapersonal space). It was also shown that the encoding of spaces has dynamic aspects because they can be remapped by the use of tools that trigger different actions (e.g., Iriki et al. 1998). In this latter case, the effect of the tool depends on the modulation of personal space, that is the space of our body. In this paper, I will review and discuss selected research, which demonstrated that also in humans: 1 spaces are encoded in a dynamic way; 2 encoding can be modulated by the use of tool that the system comes to consider as parts of the own body; 3 body representations are not fixed, but they are fragile and subject to change to the point that we can incorporate not only the tools necessary for action, but even limbs belonging to other people. What embodiment of tools and of alien limb tell us about body representations is then briefly discussed.