A rule-based expert system for inferring functional annotation

Functional annotation is the process that assigns a biological functionality to a deoxyribonucleic acid (DNA) sequence. It requires searching in huge data sets for candidates, and inferring the most appropriate features based on the information found and expert knowledge. When humans perform most of these tasks, results are of a high quality, but there is a bottleneck in processing; when experts are largely replaced by automated tools, annotation is faster but of poorer quality. Combining the automatic annotation with expert systems (ESs) can enhance the quality of the annotation, while effectively reducing experts’ workload. This paper presents INFAES, a rule-based ES developed for mimicking the human reasoning in the inference stage of the functional annotation. It integrates knowledge on Biology and heuristics about the use of Bioinformatics tools. Its development adopts state-of-the-art methodologies to facilitate the acquisition and integration of new knowledge. INFAES showed a high performance when compared to the systems developed for the first large-scale community-based critical assessment of protein function annotation (CAFA) [1].     

基于人工神经网络的葡萄病害诊断专家系统

设计了一种基于人工神经网络的葡萄病害诊断专家系统。以常见的18种主要的葡萄病害为研究对象,将专家知识转换为诊断规则,并作为学习样本输入神经网络进行训练,形成人工神经网络推理机。同时,采用知识库、规则推理和人工神经网络推理相结合的系统结构来优化专家系统,在提高专家系统自学能力的同时也提高了系统的响应速度。采用C#、Matlab和.NET技术混合编程实现专家系统,实验结果表明该系统有较高的诊断准确率并能稳定运行。该系统在Web上运行,更有利于系统的推广应用。     

Distributed memory systems for simulating artificial neural networks

In executing tasks involving intelligent information processing, the human brain performs better than the digital computer. The human brain derives its power from a large number [O(10¹¹)] of neurons which are interconnected by a dense interconnection network [O(10⁵) connections per neuron]. Artificial neural network (ANN) paradigms adopt the structure of the brain to try to emulate the intelligent information processing methods of the brain. ANN techniques are being employed to solve problems in areas such as pattern recognition, and robotic processing. Simulation of ANNs involves implementation of large number of neurons and a massive interconnection network. In this paper, we discuss various simulation models of ANNs and their implementation on distributed memory systems. Our investigations reveal that communication-efficient networks of distributed memory systems perform better than other topologies in implementing ANNs.     

Approximating support vector machine with artificial neural network for fast prediction

Support vector machine (SVM) is a powerful algorithm for classification and regression problems and is widely applied to real-world applications. However, its high computational load in the test phase makes it difficult to use in practice. In this paper, we propose hybrid neural network (HNN), a method to accelerate an SVM in the test phase by approximating the SVM. The proposed method approximates the SVM using an artificial neural network (ANN). The resulting regression function of the ANN replaces the decision function or the regression function of the SVM. Since the prediction of the ANN requires significantly less computation than that of the SVM, the proposed method yields faster test speed. The proposed method is evaluated by experiments on real-world benchmark datasets. Experimental results show that the proposed method successfully accelerates SVM in the test phase with little or no prediction loss.     

Atrial fibrillation classification with artificial neural networks

The acquired 72 normal sinus rhythm ECGs and 80 ECGs with atrial fibrillation (AF) are decomposed with ‘db10’ Daebauchies wavelets at level 6 and power spectral density was calculated for each decomposed signal with Welch method. Average power spectral density was calculated for six subbands and normalized to be used as input to the neural network. Levenberg-Marquart backpropagation feed forward neural network was built from logarithmic sigmoid transfer functions in three-layer form. The trained network was tested on 24 normal and 28 AF state ECGs. The classification performance was accomplished as 100% accurate.     

Framework for building rule-based machine diagnostic expert systems

In the endeavour to build an expert system called XBAK using Personal Consultant Plus for the diagnosis of sophisticated equipment used in microchip manufacturing, a rule-based machine diagnostic expert system architecture was developed. The approach, features and technical implementation of this application-independent problem-solving structure are described. The architecture can be used as a framework for solving similar problems in the area of machine diagnostics.     

Top-of-atmosphere flux retrievals from CERES using artificial neural networks

The Clouds and the Earth's Radiant Energy System (CERES) instruments on the Terra spacecraft provide accurate shortwave (SW), longwave (LW) and window (WN) region top-of-atmosphere (TOA) radiance measurements from which TOA radiative flux values are obtained by applying Angular Distribution Models (ADMs). These models are developed empirically as functions of the surface and cloud properties provided by coincident high-resolution imager measurements over CERES field-of-view. However, approximately 5.6% of the CERES/Terra footprints lack sufficient imager information for a reliable scene identification. To avoid any systematic biases in regional mean radiative fluxes, it is important to provide TOA fluxes for these footprints. For this purpose, we apply a feedforward error-backpropagation Artificial Neural Network (ANN) technique to reproduce CERES/Terra ADMs relying only on CERES measurements. All-sky ANN-based angular distribution models are developed for 10 surface types separately for shortwave, longwave and window TOA flux retrievals. To optimize the ANN performance, we use a partially connected first hidden neuron layer and compact training sets with reduced data noise. We demonstrate the performance of the ANN-based ADMs by comparing TOA fluxes inferred from ANN and CERES anisotropic factors. The global annual average bias in ANN-derived fluxes relative to CERES is less than 0.5% for all ANN scene types. The maximum bias occurs over sea ice and permanent snow surfaces. For all surface types, instantaneous ANN-derived TOA fluxes are self-consistent in viewing zenith angle to within 9% for shortwave, 3.5% and 3% longwave daytime and nighttime, respectively.     

A comparison between functional networks and artificial neural networks for the prediction of fishing catches

In recent years, functional networks have emerged as an extension of artificial neural networks (ANNs). In this article, we apply both network techniques to predict the catches of the Prionace Glauca (a class of shark) and the Katsowonus Pelamis (a variety of tuna, more commonly known as the Skipjack). We have developed an application that will help reduce the search time for good fishing zones and thereby increase the fleets competitivity. Our results show that, thanks to their superior learning and generalisation capacities, functional networks are more efficient than ANNs. Our data proceeds from remote sensors. Their spectral signatures allow us to calculate products that are useful for ecological modelling. After an initial phase of digital image processing, we created a database that provides all the necessary patterns to train both network types.     

Combining deterministic modelling with artificial neural networks for suspended sediment estimates

Estimates of suspended sediment concentrations and transport are an important part of any marine environment assessment study because these factors have a direct impact on the life cycle and survival of marine ecosystems. This paper proposes to implement a combined methodology to tackle these estimates. The first component of the methodology comprised two numerical current and wave models, while the second component was based on the artificial intelligence technique of neural networks (ANNs) used to reproduce values of sediment concentrations observed at two sites. The ANNs were fed with modelled currents and waves and trained to produce area-specific concentration estimates. The trained ANNs were then applied to predict sediment concentrations over an independent period of observations. The use of a data set that merged together observations from both the mentioned sites provided the best ANN testing results in terms of both the normalised root mean square error (0.13) and the mean relative error (0.02).     

RBF神经网络参数估计的两种混合优化算法

基于全局搜索的进化算法和一种局部搜索算法--结构化的非线性参数优化方法(SNPOM),提出两种混合的优化算法来估计RBF神经网络中的参数:1)初始化一定数目的种群作为SNPOM的初始值得到其适应值,通过选择、交叉和替换策略来更新种群;2)采用进化算法运行一定的代数,从最终群体中选取一些个体进一步用SNPOM来优化.这两种混合优化算法的本质是用进化算法为SNPOM搜寻最优初始值,以得到全局最优解.仿真实验结果表明,该混合算法比单独使用进化算法或SNPOM更优,且优于其他一些算法.     

Application of bacterial foraging technique trained artificial and wavelet neural networks in load forecasting

A new load forecasting (LF) approach using bacterial foraging technique (BFT) trained wavelet neural network (WNN) is proposed in this paper. Artificial neural network (ANN) is combined with wavelet transform called wavelet neural network is applied for LF. The parameters of translation and dilation in the wavelet nodes and the weighting factors in the weighting nodes are tuned using BFT optimization. With the advantages of global search abilities of BFT as well as the multiresolution and localizing natures of wavelets, the networks are constructed which identifies the inherent non-linear characteristics of power system loads. The proposed approach is validated with Tamil Nadu Electricity Board (TNEB) system, India. The comparison of Delta Rule and BFT-based LF for different periods are depicted with their mean absolute percentage errors (MAPE).     

基于假设检验和专家系统的神经网络预测

根据硅渣形成的机理及数据特点，利用统计分析的假设检验来增加数据的可靠性，并结合长期积累的专家经验知识以及采用产生式规则知识的表示形式，建立了基于主元分析的数据分类专家系统。然后针对硅渣机理的特点，构造出神经网络结构来预测硅渣的准确值。最后将该系统用计算机实现仿真实验，仿真结果表明，本文提出的基于假设检验和专家系统数据预处理的神经网络预测方法是一种可行的数据预测策略。     

一种改进的神经网络机械故障诊断专家系统

针对传统BP神经网络训练中收敛速度较慢的缺点,提出一种基于L-M算法的神经网络应用于机械设备故障诊断的专家系统。论述了神经网络的专家系统结构,并以7216圆锥轴承试验研究为例,建立了基于该算法的故障诊断模型。仿真结果表明:该模型显著缩短了训练时间,具有较高的准确性。运用该神经网络专家系统进行机械故障诊断是有效的。     

Hybrid control of the three phase induction machine using artificial neural networks and fuzzy logic

Nowadays, the microcomputer performs calculations at an incredibly high rate of billions of instructions per second. That represents an exponential increase in the processing speed since the early days of the computer development, eventhough such growth did not show complex reasoning that even the simple biological organisms can make. The artificial intelligence techniques as an attempt to work about those limitations, are a promising alternative.Each intelligent technique has its particular strengths and weaknesses and cannot be universally implemented to any problem. Mixed together, these techniques can improve the solutions quality and allow application to various tasks. It is the reason why the AI is used increasingly in order to solve complex problems in engineering. Where, it is still necessary to make progress in the controller tuning.The idea proposed in this paper is simple and original. It is the result of a study that compared the performances of two controls based on the artificial intelligence techniques: the artificial neural networks and the fuzzy logic. The control proposed in this paper combines in a different manner these two techniques in the form of a hybrid control. The aim is to benefit from performances of each of these techniques, by using them in the same control block at the most suitable place.The performances of the this proposed hybrid control; applied to the three-phase induction motor supplied by voltage source inverter; are investigated and compared to those obtained from the controls based on artificial neural networks; fuzzy logic and conventional techniques. The results of simulation show the feasibility and the good performances achieved by the proposed control.     

Hybrid expert systems: A survey of current approaches and applications

This paper is a statistical analysis of hybrid expert system approaches and their applications but more specifically connectionist and neuro-fuzzy system oriented articles are considered. The current survey of hybrid expert systems is based on the classification of articles from 1988 to 2010. Present analysis includes 91 articles from related academic journals, conference proceedings and literature reviews. Our results show an increase in the number of recent publications which is an indication of gaining popularity on the part of hybrid expert systems. This increase in the articles is mainly in neuro-fuzzy and rough neural expert systems’ areas. We also observe that many new industrial applications are developed using hybrid expert systems recently.     

Feedforward backpropagation artificial neural networks on reconfigurable meshes

The artificial neural networks (ANNs) have been used successfully in applications such as pattern recognition, image processing, automation and control. Majority of today's applications use backpropagate feedforward ANN. In this paper, two methods of P pattern L layer ANN learning on n × n RMESH have been presented. One required memory space of O(nL) but conceptually is simpler to develop and the other uses pipelined approach which reduces the memory requirement to O(L). Both of these algorithms take O(PL) time and are optimal for RMESH architecture.     

Prediction of gait speed from plantar pressure using artificial neural networks

The goal of this study was to predict gait speed over the entire cycle in reference to plantar pressure data acquired by means of the insole-type plantar pressure measuring device (Novel Pedar-x system). To predict gait speed, the artificial neural network is adopted to develop the model to predict gait speed in the stance phase (Model I) and the model to predict gait speed in the swing phase (Model II). The predicted gait speeds were validated with actual values measured using a motion capturing system (VICON 460 system) through a five-fold cross-validation method, and the correlation coefficients (R) for the gait speed were 0.963 for normal walking, 0.978 for slow walking, and 0.950 for fast walking. The method proposed in this study is expected to be widely used clinically in understanding the progress and clarifying the cause of such diseases as Parkinsonism, strike, diabetes, etc. It is expected that the method suggested in this study will be the basis for the establishment of a new research method for pathologic gait evaluation.     

应用人工神经元网络进行pH中和过程故障诊断

应用NeurOn-Line神经元网络应用系统开发技术和G2实时智能专家系统开发技术,开发了一套pH中和过程的故障诊断系统。先简单描述了该pH中和过程及其建模,然后详细论述了该故障诊断系统在NeurOn-Line和G2软件平台上的设计和编程开发情况。共进行了pH中和过程的正常运行模式,pH传感器测量值偏高、pH传感器测量值偏低和碱液浓度变稀三种故障模式的仿真和诊断。仿真结果表明该故障诊断系统能快速准确诊断出pH中和过程的正常运行和故障模式。     

Research and Design of a Fuzzy Neural Expert System

We have developed a fuzzy neural expert system that has the precision and learning ability of a neural network.Knowledge is acquired from domain experts as fuzzy rules and membership functions.Then,they are converted into a neural network which implements fuzzy inference without rule matching.The neural network is applied to problem-solving and learns from the data obtained during operation to enhance the accuracy.The learning ability of the neural network makes it easy to modify the membership functions defined by domain experts.Also,by modifying the weights of neural networks adaptively,the problem of belief propagation in conventional expert systems can be solved easily.Converting the neural network back into fuzzy rules and membership functions helps explain the inner representation and operation of the neural network.