Neural networks and linear programming for the satisfiability problem.

First a Linear Programming formulation is considered for the satisfiability problem, in particular for the satisfaction of a Conjunctive Normal Form in the Propositional Calculus and the Simplex algorithm for solving the optimization problem. The use of Recurrent Neural Networks is then described for choosing the best pivot positions and greatly improving the algorithm performance. The result of hard cases testing is reported and shows that the technique can be useful even if it requires a huge amount of size for the constraint array and Neural Network Data Input.     

人工神经网络与遗传算法相结合在作物估产中的应用——以吉林省玉米估产为例

在遗传算法(Genetic Algorithm)与误差反传(Back Propagation)网络结构模型相结合的基础上,设计了用遗传算法训练神经网络权重的新方法,并对吉林省梨树和德惠县的玉米进行了估产研究,同时与BP算法和灰色系统理论模型进行了比较.经检验,计算值与实际值接近,并优于灰色理论模型,具有良好的预测效果,从而为农作物估产提供了新方法.     

遗传算法优化真菌深层培养过程神经网络模型的研究

提出一种简易的真菌深层培养过程网络模型。输入变量为可在线测量的排气中的二氧化碳浓度,网络权数采用遗传算法进行优化训练。所获神经网络模型能准确预测培养过程的状态变量(生物量浓度,产物浓度等)。研究表明遗传算法训练此类神经网络系统是可行的。     

Rough sets and genetic algorithms in learning cellular neural networks cloning template for decision making system

We purpose to find a new beneficial method for accelerating the Decision-Making and classifier support applied on imprecise data. This acceleration can be done by integration between Rough Sets theory, which gives us the minimal set of decision rules, and the Cellular Neural Networks. Our method depends on Genetic Algorithms for designing the cloning template for more accuracy. Some illustrative examples are given to demonstrate the effectiveness of the proposed method, whose advantages and limitations are also discussed.     

采用人工生命方法模拟七星瓢虫捕食行为进化

自从 2 0世纪 70年代Ｂｕｒｋｓ[1] 提出人工生命的概念后 ,人工生命作为一个全新的研究领域 ,以其特有的优势在近些年来得到迅猛地发展。人工生命的基本思想是去构造某种人工系统以达到对生物的生长、发育、遗传、变异、生殖、进化、学习等生命过程重要特征的模拟 ,从而认清这些生命现象的本质。人工生命有广泛的应用 ,它所使用的方法也是多样的。粗略地说 ,可分为湿件 (Ｗｅｔｗａｒｅ ,意为采用化学方法模拟 )、硬件 (Ｈａｒｄｗａｒｅ ,意为用机器人模拟 )和软件 (Ｓｏｆｔｗａｒｅ ,意为用程序模拟 )。本文集中在采用软件方法进行行为…     

An FPGA hardware/software co-design towards evolvable spiking neural networks for robotics application

This paper presents an approach that permits the effective hardware realization of a novel Evolvable Spiking Neural Network (ESNN) paradigm on Field Programmable Gate Arrays (FPGAs). The ESNN possesses a hybrid learning algorithm that consists of a Spike Timing Dependent Plasticity (STDP) mechanism fused with a Genetic Algorithm (GA). The design and implementation direction utilizes the latest advancements in FPGA technology to provide a partitioned hardware/software co-design solution. The approach achieves the maximum FPGA flexibility obtainable for the ESNN paradigm. The algorithm was applied as an embedded intelligent system robotic controller to solve an autonomous navigation and obstacle avoidance problem.     

A new method for the re-implementation of threshold logic functions with cellular neural networks

A new strategy is presented for the implementation of threshold logic functions with binary-output Cellular Neural Networks (CNNs). The objective is to optimize the CNNs weights to develop a robust implementation. Hence, the concept of generative set is introduced as a convenient representation of any linearly separable Boolean function. Our analysis of threshold logic functions leads to a complete algorithm that automatically provides an optimized generative set. New weights are deduced and a more robust CNN template assuming the same function can thus be implemented. The strategy is illustrated by a detailed example.     

Unsupervised fuzzy clustering using Weighted Incremental Neural Networks

A new more efficient variant of a recently developed algorithm for unsupervised fuzzy clustering is introduced. A Weighted Incremental Neural Network (WINN) is introduced and used for this purpose. The new approach is called FC-WINN (Fuzzy Clustering using WINN). The WINN algorithm produces a net of nodes connected by edges, which reflects and preserves the topology of the input data set. Additional weights, which are proportional to the local densities in input space, are associated with the resulting nodes and edges to store useful information about the topological relations in the given input data set. A fuzziness factor, proportional to the connectedness of the net, is introduced in the system. A watershed-like procedure is used to cluster the resulting net. The number of the resulting clusters is determined by this procedure. Only two parameters must be chosen by the user for the FC-WINN algorithm to determine the resolution and the connectedness of the net. Other parameters that must be specified are those which are necessary for the used incremental neural network, which is a modified version of the Growing Neural Gas algorithm (GNG). The FC-WINN algorithm is computationally efficient when compared to other approaches for clustering large high-dimensional data sets.     

FPGA implementation of a pyramidal Weightless Neural Networks learning system

A hardware architecture of a Probabilistic Logic Neuron (PLN) is presented. The suggested model facilitates the on-chip learning of pyramidal Weightless Neural Networks using a modified probabilistic search reward/penalty training algorithm. The penalization strategy of the training algorithm depends on a predefined parameter called the probabilistic search interval. A complete Weightless Neural Network (WNN) learning system is modeled and implemented on Xilinx XC4005E Field Programmable Gate Array (FPGA), allowing its architecture to be configurable. Various experiments have been conducted to examine the feasibility and performance of the WNN learning system. Results show that the system has a fast convergence rate and good generalization ability.     

Using weighted fixed neural networks for unsupervised fuzzy clustering

A novel algorithm for unsupervised fuzzy clustering is introduced. The algorithm uses a so-called Weighted Fixed Neural Network (WFNN) to store important and useful information about the topological relations in a given data set. The algorithm produces a weighted connected net, of weighted nodes connected by weighted edges, which reflects and preserves the topology of the input data set. The weights of the nodes and the edges in the resulting net are proportional to the local densities of data samples in input space. The connectedness of the net can be changed, and the higher the connectedness of the net is chosen, the fuzzier the system becomes. The new algorithm is computationally efficient when compared to other existing methods for clustering multi-dimensional data, such as color images.     

Predicting protein solubility with a hybrid approach by pseudo amino acid composition

Protein solubility plays a major role for understanding the crystal growth and crystallization process of protein. How to predict the propensity of a protein to be soluble or to form inclusion body is a long but not fairly resolved problem. After choosing almost 10,000 protein sequences from NCBI database and eliminating the sequences with 90% homologous similarity by CD-HIT, 5692 sequences remained. By using Chou's pseudo amino acid composition features, we predict the soluble protein with the three methods: support vector machine (SVM), back propagation neural network (BP Neural Network) and hybrid method based on SVM and BP Neural Network, respectively. Each method is evaluated by re-substitution test and 10-fold cross-validation test. In the re-substitution test, the BP Neural Network performs with the best results, in which the accuracy achieves 0.9288 and Matthews Correlation Coefficient (MCC) achieves 0.8513. Meanwhile, the other two methods are better than BP Neural Network in 10-fold cross-validation test. The hybrid method based on SVM and BP Neural Network is the best. The average accuracy is 0.8678 and average MCC is 0.7233. Although all of the three methods achieve considerable evaluations, the hybrid method is deemed to be the best, according to the performance comparison.     

Analysis of multidimensional neural activity via CNN-UM

In this paper we show that the Cellular Nonlinear Network Universal Machine (CNN-UM) is an excellent tool for analyzing time series of multidimensional binary signals. The developed algorithm is dedicated to process electrophysiological multi-neuron recordings: our aim is to find specific multidimensional activity patterns, which may reflect higher order functional cell-assemblies. The analysis consists of two parts: first, the occurrences of different patterns are counted, then the statistical significance of each occurrence frequency is calculated separately.     

一类带有阈的神经网络模型的全局稳定性

利用分析技巧,获得了一类带有阈的神经网络模型全局稳定性的判据,去掉了文「１」相应结果的一个较强条件∫＾∞０ｓｋ（ｓ）ｄｓ〈＋∞。     

Identification and functional characterization of CD133+GFAP+CD117+Sca1+ neural stem cells

Molecular and Cellular Biochemistry - Neural stem cells (NSCs) are responsible for maintaining the nervous system and repairing damages. Utility of NSCs could provide a novel solution to treat...     

运用BP人工神经网络预测长江中下游梨黑星病发病的研究

提出了运用人工神经网络技术进行梨黑星病预测的新思路,并以梨黑星病发病的主要影响因素,即上年7月的降水量和上年8月的降水量作为训练样本模式提供给网络,按照误差逆传播网络的学习规则对网络进行训练,经过计算机2844次学习后,网络达到预先给定的收敛标准,使网络具备了预测梨树黑星病流行趋势和流行强度的功能。检验结果表明,该方法性能良好,预测准确率高,可望成为果树病早害预测预报的有效辅助手段。     

分子信标芯片计算在0-1整数规划问题中的应用

生物芯片技术和DNA计算分别是近年来生命科学与信息科学的新兴研究领域,对信息高度并行的获取与处理是二者的本质特性．而0-1整数规划问题作为运筹学中一个重要的问题,到目前为止还没有好的算法．在DNA计算和DNA芯片基础上,提出了基于分子信标芯片解决0-1整数规划问题的DNA计算新模型．与以往DNA计算模型相比,该模型具有高信息量和操作易自动化的优点,同时指出分子信标芯片技术有望作为新型生物计算的芯片．