Law,discipline and governance based on cognitive process simulation – analysis of legal forms in the modern rights relationship

People are becoming more and more humanized in the process of understanding the law. According to the right to discipline, the law has its own core setting factors, while some limits can't reach people's desire. Therefore, the legal and illegal mode of transcending rights is very important. In order to analyze the legal form of modern rights, in this paper, the cognitive learning and memory process of human brain were simulated through the artificial neural network and the understanding of human brain structure, and the role of law, discipline and governance was reflected. In the study, the structure and algorithm of the model neural network were optimized, the memory forgetting curve mechanism that can simulate the human brain was introduced, and thus the network recognition rate was improved. And in the algorithm, the calculation of matching degree was avoided, and the computational complexity was reduced to the sample. Then the sample was compared with the SOM, ART1, and PNN algorithms. The experimental simulation results show that the recognition speed of this sample is 1.9 times faster than that of ART1, 58 times than that of SOM, and 1.5 times than that of the PNN network.     

The performance space measurement of regional innovation system based on neuropsychology

In this paper, the performance space measurement of regional innovation system was studied based on neuropsychology. Firstly, the neuropsychology and neural evolution theory were elaborated. Secondly, the genetic algorithm was used to design a regional enterprise performance space measurement model, and this was obtained by connecting the ERP production module and RBF neural network order forecast module. Finally, the algorithm and model constructed in this paper were used to predict the performance of regional foreign trade innovation system. Then, it is concluded that the model constructed in this paper includes the network with the lowest network structure complexity, the smallest training error and the least test error. Therefore, based on this premise, a good neural network that meets the actual needs of users can be obtained, which indicates that the improved method based on evolutionary neural network is effective to measure the performance of regional innovation system.     

一种可融入额外信息的机器学习诊断法

研究将PNN和曼哈顿距离、贝叶斯定理相结合,提出了一种相对简洁的可融入额外信息的认知诊断法MB-PNN,通过模拟和实证研究考察了MB-PNN的有效性和适宜性,得到以下结论:(1)M-PNN的判准率高于PNN,表明将PNN中的ED修改为MD是适宜的;(2)MB-PNN的判准率较M-PNN和PNN高,表明基于多种信息的判别较基于单一信息的判别更为精准;(3)MB-PNN保留了PNN原有的非参数优势,基本不受知识状态分布和样本容量影响;(4)MB-PNN最能区分不同类型的学生,在认知诊断评估实践中更为适宜。     

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.     

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.     

汉语句子格角色分配的一种神经网络方法

提出了一个基于分布式表征的计算模型,通过并行分布加工方式完成六类汉语句子的格角色分配任务。模型是一个四层的前传网络,包括输入层（词的分布式表征层）,两个隐层,输出层（格角色层）;其中第一隐层的一部分反馈到输入层。模型采用误差反传算法,通过提供学习样本和目标输出,不断调整三个权值矩阵,使得网络稳定时能得到正确的结果。经过训练后的网络具有一定的稳定性和鲁棒性。还对这种方法与传统的符号处理方法作了比较和分析。     

基于联结主义的连续记分IRT模型的项目参数和被试能力估计

运用联结主义中的级连相关模型对于小样本条件下的连续记分项目反应理论 (IRT)模型的项目参数和被试能力进行了估计。一组被试对于一组项目的反应矩阵作为级连相关模型的输入 ,这组被试的能力θ或该组项目的参数a、b和c作为该模型的输出 ,对神经网络进行训练使之具备了估计θ,a ,b或c的能力。计算机模拟的实验表明 ,如果测验中有少量项目取自于题库 ,就可以运用联结主义方法对IRT参数和被试能力进行较好的估计     

Electrophysiology in neuropsychiatric research: a network perspective

A growing number of anatomic and physiologic studies have shown that parallel sensory and motor information processing occurs in multiple cortical areas. These findings challenge the traditional model of brain processing, which states that the brain is a collection of physically discrete processing modules that pass information to each other by neuronal impulses in a stepwise manner. New concepts based on neural network models suggest that the brain is a dynamically shifting collection of interpenetrating, distributed, and transient neural networks. Neither of these models is necessarily mutually exclusive, but each gives different perspectives on the brain that might be complementary. Each model has its own research methodology, with functional magnetic resonance imaging supporting notions of modular processing, and electrophysiology (eg, electroencephalography) emphasizing the network model. These two technologies might be combined fruitfully in the near future to provide us with a better understanding of the brain. However, this common enterprise can succeed only when the inherent limitations and advantages of both models and technologies are known. After a general introduction about electrophysiology as a research tool and its relation to the network model, several practical examples are given on the generation of pathophysiologic models and disease classification, intermediate phenotyping for genetic investigations, and pharmacodynamic modeling. Finally, proposals are made about how to integrate electrophysiology and neuroimaging methods.     

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.     

拖延行为的认知神经模型及干预

拖延是一种普遍存在, 具有跨时间和跨情景稳定性的问题行为, 它会危害到人们的学习、工作和身心健康。然而目前拖延行为的认知神经机制仍不清晰, 且缺乏因果证据, 本项目拟从拖延的时间决策模型和三重神经结构网络模型出发, 构建拖延的认知神经模型, 并利用认知干预和神经调控技术, 检验和完善拖延行为的认知神经模型, 进而试图制定拖延的精准化干预方案。本项目分为3部分：(1)从记录与关联研究的视角出发, 利用多模态神经影像方法系统考察拖延行为的认知神经机制; (2)从因果/近因果研究视角出发, 利用认知干预和神经调控技术, 验证并完善拖延的认知神经模型; (3)从临床应用的视角出发, 建立拖延行为障碍的临床筛查-诊断体系, 并制定精准化治疗方案。本项目的开展对于探明拖延产生的核心认知神经机制具有十分重要的理论贡献, 同时对于拖延行为的有效预防和精准治疗具有重要的现实意义。     

On portfolio management with value at risk and uncertain returns via an artificial neural network scheme

This paper focuses on the computation issue of portfolio optimization with scenario-based Value-at-Risk. The main idea is to replace the portfolio selection models with linear programming problems. According to the convex optimization theory and some concepts of ordinary differential equations, a neural network model for solving linear programming problems is presented. The equilibrium point of the proposed model is proved to be equivalent to the optimal solution of the original problem. It is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the portfolio selection problem with uncertain returns. Several illustrative examples are provided to show the feasibility and the efficiency of the proposed method in this paper.     

Is it worth generating rules from neural network ensembles?

Although many authors generated comprehensible models from individual networks, much less work has been done in the explanation of ensembles. DIMLP is a special neural network model from which rules are generated at the level of a single network and also at the level of an ensemble of networks. We applied ensembles of 25 DIMLP networks to several datasets of the public domain and a classification problem related to post-translational modifications of proteins. For the classification problems of the public domain, the average predictive accuracy of rulesets extracted from ensembles of neural networks was significantly better than the average predictive accuracy of rulesets generated from ensembles of decision trees. By varying the architectures of DIMLP networks we found that the average predictive accuracy of rules, as well as their complexity were quite stable. The comparison to other rule extraction techniques applied to neural networks showed that rules generated from DIMLP ensembles gave very good results. In the last problem related to bioinformatics, the best result obtained by ensembles of DIMLP networks was also significantly better than the best result obtained by ensembles of decision trees. Thus, although neural networks take much longer to train than decision trees and also rules are generated at a greater computational cost (however, still polynomial), at least for several classification problems it was worth using neural network ensembles, as extracted rules were more accurate, on average. The DIMLP software is available for PC-Linux under http://us.expasy.org/people/Guido.Bologna.html.     

The translation and introduction of Mo Yan's works based on neurolinguistics

As an advanced function of the cognitive neural mechanism of human brain, inductive reasoning is an important skill in language communication. Under the background of the development of information intelligence, it is a new research field to effectively display the cognitive neural function of inductive reasoning with the advantage of the logic operation of artificial intelligence algorithm. Therefore, in this paper, based on the neurolinguistics, the translation and introduction of Mo Yan's works were studied. And on the basis of the analysis of the characteristics of the cognitive neural mechanism of sentence inductive reasoning, by using fMR and ERP techniques, the narrowing characteristics of the semantic integrated components in the induction were investigated, and the dual processing model of inductive reasoning was discussed. After that, artificial intelligence particle swarm optimization (PSO) algorithm was introduced, and the problem of alignment in the translation of English and Chinese sentences in Mo Yan's works was transformed into the problem of finding the optimal solution for the corresponding fitness function in Chinese and English sentences in bilingual space. Thus, a scientific mathematical model was used to improve the accuracy of translation. The simulation experiments show that this study can effectively improve the accuracy of the translation and introduction of Mo Yan's works.     

What causes the effect of age of acquisition in lexical processing?

Three hypotheses for effects of age of acquisition (AoA) in lexical processing are compared: the cumulative frequency hypothesis (frequency and AoA both influence the number of encounters with a word, which influences processing speed), the semantic hypothesis (early-acquired words are processed faster because they are more central in the semantic network), and the neural network model (early-acquired words are faster because they are acquired when a network has maximum plasticity). In a regression study of lexical decision (LD) and semantic categorization (SC) in Italian and Dutch, contrary to the cumulative frequency hypothesis, AoA coefficients were larger than frequency coefficients, and, contrary to the semantic hypothesis, the effect of AoA was not larger in SC than in LD. The neural network model was supported.     

Comparing two methods of visual abstraction to human data

The ability to “visually abstract” a given pattern with a neural network and abstract the same pattern by using a regression/correlation analysis was investigated. Both methods were compared with human subjects performing the same task. To visually abstract a particular shape, both quantitative methods broke the shape down into its linear, quadratic, and cubic components. Using an IBM-compatible personal computer, 10 test patterns were analyzed with a neural network (designed using Brainmaker Professional and trained with known linear, quadratic, and cubic shapes) and a regression/correlation model (designed using Lotus 1-2-3). The 10 test patterns were also analyzed by 22 human subjects. The neural network data were found to be highly correlated with the human data [r(8) = .90,p < .01]. The regression/correlation model’s data were also found to be significantly correlated with the human data [r(8) = .77,p < .01]. These findings demonstrate the successful modeling of Rumelhart’s (1991) regression/correlation approach to visual abstraction.     

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.     

What causes the effect of age of acquisition in lexical processing?

Three hypotheses for effects of age of acquisition (AoA) in lexical processing are compared: the cumulative frequency hypothesis (frequency and AoA both influence the number of encounters with a word, which influences processing speed), the semantic hypothesis (early-acquired words are processed faster because they are more central in the semantic network), and the neural network model (early-acquired words are faster because they are acquired when a network has maximum plasticity). In a regression study of lexical decision (LD) and semantic categorization (SC) in Italian and Dutch, contrary to the cumulative frequency hypothesis, AoA coefficients were larger than frequency coefficients, and, contrary to the semantic hypothesis, the effect of AoA was not larger in SC than in LD. The neural network model was supported.     

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.     

Readout from iconic memory and selective spatial attention involve similar neural processes

Iconic memory and spatial attention are often considered separately, but they may have functional similarities. Here we provide functional magnetic resonance imaging evidence for some common underlying neural effects. Subjects judged three visual stimuli in one hemifield of a bilateral array comprising six stimuli. The relevant hemifield for partial report was indicated by an auditory cue, administered either before the visual array (precue, spatial attention) or shortly after the array (postcue, iconic memory). Pre- and postcues led to similar activity modulations in lateral occipital cortex contralateral to the cued side. This finding indicates that readout from iconic memory can have some neural effects similar to those of spatial attention. We also found common bilateral activation of a fronto-parietal network for postcue and precue trials. These neuroimaging data suggest that some common neural mechanisms underlie selective spatial attention and readout from iconic memory. Some differences were also found; compared with precues, postcues led to higher activity in the right middle frontal gyrus.     

多维项目反应理论补偿性模型参数估计:基于广义回归神经网络集合

运用广义回归神经网络(GRNN)方法对小样本多维项目反应理论(MIRT)补偿性模型的项目参数进行估计,尝试解决传统参数估计方法样本数量要求较大的问题。MIRT双参数Logistic补偿模型被设置为二级计分的二维模型。首先,模拟二维能力参数、项目参数值与考生作答矩阵。其次,把通过主成分分析得到的前两个因子在每个题目上的载荷作为区分度的初始值以及题目通过率作为难度的初始值,这两个指标的初始值作为神经网络的输入。集成100个神经网络,其输出值的均值作为MIRT的项目参数估计值。最后,设置2×2种(能力相关水平:0.3和0.7; 两种估计方法:GRNN和MCMC方法)实验处理,对GRNN和MCMC估计方法的返真性进行比较。结果表明,小样本的情况下,基于GRNN集成方法的参数估计结果优于MCMC方法。