Lossless data compression scheme based on neural network and SVM

First, the compression-awaited data are regarded Lis character strings which are produced by virtual information source mapping M. then the model of the virtual information source M is established by neural network and SVM. Last we construct a lossless data compression (coding) scheme based oil neural network and SVM with the model, an integer function and a SVM discriminant. The scheme differs from the old entropy coding (compressions) inwardly, and it can compress some data compressed by the old entropy coding.

Structure approximate based on double weighted neural network

Double weighted neural network; is a kind of new general used neural network, which, compared with BP and RBF network, may approximate the training samples with a move complicated geometric figure and possesses a even greater approximation. capability. we study structure approximate based on double weighted neural network and prove its rationality.

Artificial neural networks expecting more advanced microelectronics technology

　

Global stability of Hopfield neural networks

This paper gives a condition for the global stability of a continuous-time hopfield neural network when its activation function maybe not monotonically increasing.

Retention modeling and simultaneous optimization of pH value and gradient steepness in RP-HPLC using feed-forward neural networks

A novel approach is proposed for the simultaneous optimization of mobile phase pH and gradient steepness in RP-HPLC using artificial neural networks. By presetting the initial and final concentration of the organic solvent, a limited number of experiments with different gradient time and pH value of mobile phase are arranged in the two-dimensional space of mobile phase parameters. The retention behavior of each solute is modeled using an individual artificial neural network. An "early stopping" strategy is adopted to ensure the predicting capability of neural networks. The trained neural networks can be used to predict the retention time of solutes under arbitrary mobile phase conditions in the optimization region. Finally, the optimal separation conditions can be found according to a global resolution function. The effectiveness of this method is validated by optimization of separation conditions for amino acids derivatised by a new fluorescent reagent.

Application of probabilistic neural network in the clinical diagnosis of cancers based on clinical chemistry data

As a recently developed and powerful classification tool, probabilistic neural network was used to distinguish cancer patients from healthy persons according to the levels of nucleosides in human urine. Two datasets (containing 32 and 50 patterns, respectively) were investigated and the total consistency rate obtained was 100% for dataset 1 and 94% for dataset 2. To evaluate the performance of probabilistic neural network, linear discriminant analysis and learning vector quantization network, were also applied to the classification problem. The results showed that the predictive ability of the probabilistic neural network is stronger than the others in this study. Moreover, the recognition rate for dataset 2 can achieve to 100% if combining, these three methods together, which indicated the promising potential of clinical diagnosis by combining different methods. (C) 2002 Elsevier Science B.V. All rights reserved.

Simultaneous determination of methylene blue and new methylene blue by slab optical waveguide spectroscopy and artificial neural networks

A slab optical waveguide (SOWG) has been used for study of adsorption of both methylene blue (MB) and new methylene blue (NMB) in liquid-solid interface. Adsorption characteristics of MB and NMB on both bare SOWG and silanized SOWG by octadecyltrichlorosilane (ODS) were compared. The simultaneous determinations of both MB and NMB were explored by flow injection SOWG spectrophotometric analysis and artificial neural networks (ANNs) for the first time. Concentrations of MB and NMB were estimated simultaneously with the ANNs. Results obtained with SOWG were compared with those got by conventional UV-visible spectrophotometry. (C) 2003 Elsevier Science B.V All rights reserved.

Classification of valence changes of trivalent rare earth ions in alkaline earth borates using artificial neural networks

The investigations of classification on the valence changes from RE3+ to RE2+ (RE = Eu, Sm, Yb, Tm) in host compounds of alkaline earth berate were performed using artificial neural networks (ANNs). For comparison, the common methods of pattern recognition, such as SIMCA, KNN, Fisher discriminant analysis and stepwise discriminant analysis were adopted. A learning set consisting of 24 host compounds and a test set consisting of 12 host compounds were characterized by eight crystal structure parameters. These parameters were reduced from 8 to 4 by leaps and bounds algorithm. The recognition rates from 87.5 to 95.8% and prediction capabilities from 75.0 to 91.7% were obtained. The results provided by ANN method were better than that achieved by the other four methods. (C) 1999 Elsevier Science B.V. All rights reserved.

Prediction of molar absorptivities of color reagents during their color reactions with cerium using multiple regression analysis and neural network

A quantitative structure-property study has been made on the relationship between molar absorptivities (epsilon) of asymmetrical phosphone bisazo derivatives of chromotropic acid and their color reactions with cerium by multiple regression analysis and neural network. The new topological indices A(x1) - A(x3) suggested in our laboratory and molecular connectivity indices of 43 compounds have been calculated. The results obtained from the two methods are compared. The neural network model is superior to the regression analysis technique and gave a prediction which was sufficiently accurate to estimate the molar absorptivities of color reagents during their color reactions with cerium.

Study on Phenol Compounds Using Multiple Regression and Neural Network Methods

In this paper, the molecular connectivity indices and the electronic charge parameters of forty-eight phenol compounds nave been calculated. and applied for studying the relationship between partition coefficients and structure of phenol compounds. The results demonstrate that the properties of compounds can be described better with selective parameters, and the results obtained by neural network are superior to that by multiplle regression.

Neural network retrieval of ocean surface parameters from SSM/I data

A new algorithm based on the multiparameter neural network is proposed to retrieve wind speed (WS), sea surface temperature (SST), sea surface air temperature, and relative humidity ( RH) simultaneously over the global oceans from Special Sensor Microwave Imager (SSM/I) observations. The retrieved geophysical parameters are used to estimate the surface latent heat flux and sensible heat flux using a bulk method over the global oceans. The neural network is trained and validated with the matchups of SSM/I overpasses and National Data Buoy Center buoys under both clear and cloudy weather conditions. In addition, the data acquired by the 85.5-GHz channels of SSM/I are used as the input variables of the neural network to improve its performance. The root-mean-square (rms) errors between the estimated WS, SST, sea surface air temperature, and RH from SSM/I observations and the buoy measurements are 1.48 m s(-1), 1.54 degrees C, 1.47 degrees C, and 7.85, respectively. The rms errors between the estimated latent and sensible heat fluxes from SSM/I observations and the Xisha Island ( in the South China Sea) measurements are 3.21 and 30.54 W m(-2), whereas those between the SSM/ I estimates and the buoy data are 4.9 and 37.85 W m(-2), respectively. Both of these errors ( those for WS, SST, and sea surface air temperature, in particular) are smaller than those by previous retrieval algorithms of SSM/ I observations over the global oceans. Unlike previous methods, the present algorithm is capable of producing near-real-time estimates of surface latent and sensible heat fluxes for the global oceans from SSM/I data.

锦屏电站地下厂房卸荷岩体参数研究

It is a basic work to ascertain the parameters of rock mass for evaluation about stability of the engineering. Anisotropism、inhomogeneity and discontinuity characters of the rock mass arise from the existing of the structural plane. Subjected to water、weathering effect、off-loading, mechanical characters of the rock mass are greatly different from rock itself, Determining mechanical parameters of the rock mass becomes so difficult because of structure effect、dimension effect、rheological character, ‘Can’t give a proper parameter’ becomes one of big problems for theoretic analysis and numerical simulation. With the increment of project scale, appraising the project rock mass and ascertaining the parameters of rock mass becomes more and more important and strict. Consequently, researching the parameters of rock mass has important theoretical significance and actual meaning. The Jin-ping hydroelectric station is the first highest hyperbolic arch dam in the world under construction, the height of the dam is about 305m, it is the biggest hydroelectric station at lower reaches of Yalong river. The length of underground factory building is 204.52m, the total height of it is 68.83m, the maximum of span clearance is 28.90m. Large-scale excavation in the underground factory of Jin-ping hydroelectric station has brought many kinds of destructive phenomenon, such as relaxation、spilling, providing a precious chance for study of unloading parameter about rock mass. As we all know, Southwest is the most important hydroelectric power base in China, the construction of the hydroelectric station mostly concentrate at high mountain and gorge area, basically and importantly, we must be familiar with the physical and mechanical character of the rock mass to guarantee to exploit safely、efficiently、quickly, in other words, we must understand the strength and deformation character of the rock mass. Based on enough fieldwork of geological investigation, we study the parameter of unloading rock mass on condition that we obtain abundant information, which is not only important for the construction of Jin-ping hydroelectric station, but also for the construction of other big hydroelectric station similar with Jin-ping. This paper adopt geological analysis、test data analysis、experience analysis、theory research and Artificial Neural Networks （ANN） brainpower analysis to evaluate the mechanical parameter, the major production is as follows: (1)Through the excavation of upper 5-layer of the underground powerhouse and the statistical classification of the main joints fractures exposed, We believe that there are three sets of joints, the first group is lay fracture, the second group and the fourth group are steep fracture. These provide a strong foundation for the following calculation of and analysis; (2)According to the in-situ measurement about sound wave velocity、displacement and anchor stress, we analyses the effects of rock unloading effect，the results show a obvious time-related character and localization features of rock deformation. We determine the depth of excavation unloading of underground factory wall based on this. Determining the rock mass parameters according to the measurement about sound wave velocity with characters of low- disturbing、dynamic on the spot, the result can really reflect the original state, this chapter approximately the mechanical parameters about rock mass at each unloading area; (3)Based on Hoek-Brown experienced formula with geological strength index GSI and RMR method to evaluate the mechanical parameters of different degree weathering and unloading rock mass about underground factory, Both of evaluation result are more satisfied; (4)From the perspective of far-field stress, based on the stress field distribution ideas of two-crack at any load conditions proposed by Fazil Erdogan (1962)，using the strain energy density factor criterion (S criterion) proposed by Xue changming(1972)，we establish the corresponding relationship between far-field stress and crack tip stress field, derive the integrated intensity criterion formula under the conditions of pure tensile stress among two line coplanar intermittent jointed rock，and establish the corresponding intensity criterion for the exploratory attempt; (5)With artificial neural network, the paper focuses on the mechanical parameters of rock mass that we concerned about and the whole process of prediction of deformation parameters, discusses the prospect of applying in assessment about the parameters of rock mass，and rely on the catalog information of underground powerhouse of Jinping I Hydropower Station, identifying the rock mechanics parameters intellectually，discusses the sample selection, network design, values of basic parameters and error analysis comprehensively. There is a certain significance for us to set up a set of parameters evaluation system，which is in construction of large-scale hydropower among a group of marble mass.

Neural Network Exploration Using Optimal Experiment Design

We consider the question "How should one act when the only goal is to learn as much as possible?" Building on the theoretical results of Fedorov [1972] and MacKay [1992], we apply techniques from Optimal Experiment Design (OED) to guide the query/action selection of a neural network learner. We demonstrate that these techniques allow the learner to minimize its generalization error by exploring its domain efficiently and completely. We conclude that, while not a panacea, OED-based query/action has much to offer, especially in domains where its high computational costs can be tolerated.

An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network

P-glycoprotein (P-gp), an ATP-binding cassette (ABC) transporter, functions as a biological barrier by extruding cytotoxic agents out of cells, resulting in an obstacle in chemotherapeutic treatment of cancer. In order to aid in the development of potential P-gp inhibitors, we constructed a quantitative structure-activity relationship (QSAR) model of flavonoids as P-gp inhibitors based on Bayesian-regularized neural network (BRNN). A dataset of 57 flavonoids collected from a literature binding to the C-terminal nucleotide-binding domain of mouse P-gp was compiled. The predictive ability of the model was assessed using a test set that was independent of the training set, which showed a standard error of prediction of 0.146 +/- 0.006 (data scaled from 0 to 1). Meanwhile, two other mathematical tools, back-propagation neural network (BPNN) and partial least squares (PLS) were also attempted to build QSAR models. The BRNN provided slightly better results for the test set compared to BPNN, but the difference was not significant according to F-statistic at p = 0.05. The PLS failed to build a reliable model in the present study. Our study indicates that the BRNN-based in silico model has good potential in facilitating the prediction of P-gp flavonoid inhibitors and might be applied in further drug design.

Meta-stable memory in an artificial immune network

Neal, M., Meta-stable memory in an artificial immune network, Proceedings of the 2nd International Conference on Artificial Immune Systems {ICARIS}, Springer, 168-180, 2003,LNCS 2787/2003