Artificial neural networks for robotics coordinate transformation

Artificial neural networks with such characteristics as learning, graceful degradation, and speed inherent to parallel distributed architectures might provide a flexible and cost solution to the real time control of robotics systems. In this investigation artificial neural networks are presented for the coordinate transformation mapping of a two-axis robot modeled with Fischertechnik physical modeling components. The results indicate that artificial neural systems could be utilized for practical situations and that extended research in these neural structures could provide adaptive architectures for dynamic robotics control.     

Artificial neural networks for job shop simulation

This paper explores the use of artificial neural networks (ANNs) as a valid alternative to the traditional job-shop simulation approach. Feed forward, multi-layered neural network metamodels were trained through the back-error-propagation (BEP) learning algorithm to provide a versatile job-shop scheduling analysis framework. The constructed neural network architectures were capable of satisfactorily estimating the manufacturing lead times (MLT) for orders simultaneously processed in a four-machine job shop. The MLTs produced by the developed ANN models turned out to be as valid as the data generated from three well-known simulation packages, i.e. Arena, SIMAN, and ProModel. The ANN outputs proved not to be substantially different from the results provided by other valid models such as SIMAN and ProModel when compared against the adopted baseline, Arena. The ANN-based simulations were able to fairly capture the underlying relationship between jobs' machine sequences and their resulting average flowtimes, which proves that ANNs are a viable tool for stochastic simulation metamodeling.     

Artificial neural networks for molecular sequence analysis

Artificial neural networks provide a unique computing architecture whose potential has attracted interest from researchers across different disciplines. As a technique for computational analysis, neural network technology is very well suited for the analysis of molecular sequence data. It has been applied successfully to a variety of problems, ranging from gene identification, to protein structure prediction and sequence classification. This article provides an overview of major neural network paradigms, discusses design issues, and reviews current applications in DNA/RNA and protein sequence analysis.     

Dynamic modelling using neural networks

A neural network structure is presented that uses feedback of unmeasured system states to represent dynamic systems more efficiently than conventional feedforward and recurrent networks, leading to better predictions, reduced training requirement and more reliable extrapolation. The structure identifies the actual system states based on imperfect knowledge of the initial state, which is available in many practical systems, and is therefore applicable only to such systems. It also enables a natural integration of any available partial state-space model directly into the prediction scheme, to achieve further performance improvement. Simulation examples of three varied dynamic systems illustrate the various options and advantages offered by the state-feedback neural structure. Although the advantages of the proposed structure, compared with the conventional feedforward and recurrent networks, should hold for most practical dynamic systems, artificial systems can readily be created and real systems can surely be found for which one or more of these advantages would vanish or even get reversed. Caution is therefore recommended against interpreting the suggested advantages as strict theorems valid in all situations.     

Artificial neural networks for document analysis and recognition

Artificial neural networks have been extensively applied to document analysis and recognition. Most efforts have been devoted to the recognition of isolated handwritten and printed characters with widely recognized successful results. However, many other document processing tasks, like preprocessing, layout analysis, character segmentation, word recognition, and signature verification, have been effectively faced with very promising results. This paper surveys the most significant problems in the area of offline document image processing, where connectionist-based approaches have been applied. Similarities and differences between approaches belonging to different categories are discussed. A particular emphasis is given on the crucial role of prior knowledge for the conception of both appropriate architectures and learning algorithms. Finally, the paper provides a critical analysts on the reviewed approaches and depicts the most promising research guidelines in the field. In particular, a second generation of connectionist-based models are foreseen which are based on appropriate graphical representations of the learning environment.     

Artificial neural networks for flexible manufacturing systems scheduling

Artificial neural networks (ANNs) are information processing systems motivated by the goals of reproducing the cognitive processes and organizational models of neurobiological systems. By virtue of their computational structure, ANN's feature attractive characteristics such as graceful degradation, robust recall with noisy and fragmented data, parallel distributed processing, generalization to patterns outside of the training set, nonlinear modeling capabilities, and learning. These computational features could provide enhanced inferencing functionality and real-time capabilities to develop approaches for traditional difficult problems such as flexible manufacturing system (FMS) scheduling. In this paper three different schemes of ANN's are applied to the FMS scheduling problem. These include a) relaxation-based networks, b) competitive-based schemes, and c) adaptive pattern recognition scheduling.     

Compartmental modelling with artificial neural networks

In this paper, we introduce an abbreviated compartmental modelling scheme which may be of interest to those in neuron- based adaptive systems because of the additional scope it provides for studying biologically-inspired learning mechanisms. The scheme, although not as flexible and precise as the general compartmental approach, allows one to design Hodgkin-Huxley style cells, and passive dendritic trees with an arbitrary number of synaptic connections. The trade-offs made for computational performance, may make the modelling scheme more appropriate for practical applications. The modelling scheme is based upon artificial neural networks, which we have used to represent cylindrical compartments (both passive and active) of different lengths, two types of voltage-dependent channels, and basic chemical synapses with variable time constants.     

Dynamic image modelling by neural networks

Sometimes an image can be characterized with the help of a dynamic system in terms of a first-order differential equation, e.g. when a scanner output contains additive noise and/or is degraded due to interaction between the sensing elements (the cameras) or by other phenomena. In this paper, an algorithm for image estimation from the noisy output of a scanner is hypothesized. Determination of the parameters A, B, and the C matrices of a dynamic system leads to the design of an estimator whose input is the output of a scanner with uniform speed. A portion of the overall algorithm using neural networks trained by a gradient descent learning algorithm is discussed in detail. In particular, the determination of A. B, C from states, time derivatives of states and inputs are highlighted. The details of implementation are included. The salient points regarding the development of the complete algorithm are discussed. It is hoped that the results achieved so far will give rise to new techniques for the application of neural networks to image enhancement in particular and to image processing in general.     

Artificial neural networks: a tutorial

Artificial neural nets (ANNs) are massively parallel systems with large numbers of interconnected simple processors. The article discusses the motivations behind the development of ANNs and describes the basic biological neuron and the artificial computational model. It outlines network architectures and learning processes, and presents some of the most commonly used ANN models. It concludes with character recognition, a successful ANN application     

Artificial neural networks for solving ordinary and partialdifferential equations

We present a method to solve initial and boundary value problems using artificial neural networks. A trial solution of the differential equation is written as a sum of two parts. The first part satisfies the initial/boundary conditions and contains no adjustable parameters. The second part is constructed so as not to affect the initial/boundary conditions. This part involves a feedforward neural network containing adjustable parameters (the weights). Hence by construction the initial/boundary conditions are satisfied and the network is trained to satisfy the differential equation. The applicability of this approach ranges from single ordinary differential equations (ODE), to systems of coupled ODE and also to partial differential equations (PDE). In this article, we illustrate the method by solving a variety of model problems and present comparisons with solutions obtained using the Galerkin finite element method for several cases of partial differential equations. With the advent of neuroprocessors and digital signal processors the method becomes particularly interesting due to the expected essential gains in the execution speed.     

Artificial neural networks for modeling in reaction process systems

This work is focused on developing a more efficient computational scheme for estimation of process reaction rates based on NN models. Two scenarios are considered: (1) the kinetics coefficients of the process are completely known and the process states are partly known (measured); (2) the kinetics coefficients and the states of the process are partly known. The contribution of the paper is twofold. From one side we formulate a hybrid (ANN and mechanistic) model that outperforms the traditional reaction rate estimation approaches. From other side, a new procedure for NN supervised training is proposed when target outputs are not available. The two scenarios are successfully tested for two benchmark problems, estimation of the precipitation rate of calcium phosphate and estimation of sugar crystallization growth rate.     

城市交通信号的ANN自校正预测控制

提出一种基于人工神经网络的城市交通信号的自校正预测控制方法.充分考虑相邻交叉路口之间交通流的强耦合性,在此基础上建立关于队长的交通模型;其中,受控路口下一周期到达的车辆数用人工神经网络(ANN)来预测;通过该ANN还可获得确定最佳周期长度所需要的交通参量,因此还可预测下一周期的长度;上述预测值均用实测信息进行反馈校正,在此基础上即可给出带约束的预测控制算法,从而确定下一周期的控制策略.仿真实例表明该方法具有较好的控制效果.     

Artificial neural networks for optimization of gold-bearing slime smelting

Pyrometallurgy is often used in the industrial process for treating gold-bearing slime. Slag compositions have remarkable influences on the recovery of gold and the gold content in slag. A method for determining optimum flux compounding with neural networks is studied in this paper, and the neural network model for estimating the gold contents with different slag compositions is presented. On the basis of the neural network model, an algorithm for searching the optimum flux compounding in the gold-slime smelting process is proposed, and the optimum flux compositions are obtained accordingly.     

Artificial neural networks for feature extraction and multivariatedata projection

Classical feature extraction and data projection methods have been well studied in the pattern recognition and exploratory data analysis literature. We propose a number of networks and learning algorithms which provide new or alternative tools for feature extraction and data projection. These networks include a network (SAMANN) for J.W. Sammon's (1969) nonlinear projection, a linear discriminant analysis (LDA) network, a nonlinear discriminant analysis (NDA) network, and a network for nonlinear projection (NP-SOM) based on Kohonen's self-organizing map. A common attribute of these networks is that they all employ adaptive learning algorithms which makes them suitable in some environments where the distribution of patterns in feature space changes with respect to time. The availability of these networks also facilitates hardware implementation of well-known classical feature extraction and projection approaches. Moreover, the SAMANN network offers the generalization ability of projecting new data, which is not present in the original Sammon's projection algorithm; the NDA method and NP-SOM network provide new powerful approaches for visualizing high dimensional data. We evaluate five representative neural networks for feature extraction and data projection based on a visual judgement of the two-dimensional projection maps and three quantitative criteria on eight data sets with various properties.     

Municipal credit rating modelling by neural networks

The paper presents the modelling possibilities of neural networks on a complex real-world problem, i.e. municipal credit rating modelling. First, current approaches in credit rating modelling are introduced. Second, previous studies on municipal credit rating modelling are analyzed. Based on this analysis, the model is designed to classify US municipalities (located in the State of Connecticut) into rating classes. The model includes data pre-processing, the selection process of input variables, and the design of various neural networks' structures for classification. The selection of input variables is realized using genetic algorithms. The input variables are extracted from financial statements and statistical reports in line with previous studies. These variables represent the inputs of neural networks, while the rating classes from Moody's rating agency stand for the outputs. In addition to exact rating classes, data are also labelled by four basic rating classes. As a result, the classification accuracies and the contributions of input variables are studied for the different number of classes. The results show that the rating classes assigned to bond issuers can be classified with a high accuracy rate using a limited subset of input variables.     

A taxonomy for wavelet neural networks applied to nonlinear modelling

This article presents a novel classification of wavelet neural networks based on the orthogonality/non-orthogonality of neurons and the type of nonlinearity employed. On the basis of this classification different network types are studied and their characteristics illustrated by means of simple one-dimensional nonlinear examples. For multidimensional problems, which are affected by the curse of dimensionality, the idea of spherical wavelet functions is considered. The behaviour of these networks is also studied for modelling of a low-dimension map.     

Artificial neural networks and multicriterion analysis for sustainable irrigation planning

The objective of the present paper is to select the best compromise irrigation planning strategy for the case study of Jayakwadi irrigation project, Maharashtra, India. Four-phase methodology is employed. In phase 1, separate linear programming (LP) models are formulated for the three objectives, namely, net economic benefits, agricultural production and labour employment. In phase 2, nondominated (compromise) irrigation planning strategies are generated using the constraint method of multiobjective optimisation. In phase 3, Kohonen neural networks (KNN) based classification algorithm is employed to sort nondominated irrigation planning strategies into smaller groups. In phase 4, multicriterion analysis (MCA) technique, namely, Compromise Programming is applied to rank strategies obtained from phase 3. It is concluded that the above integrated methodology is effective for modeling multiobjective irrigation planning problems and the present approach can be extended to situations where number of irrigation planning strategies are even large in number.