This paper reports on advances in identification of relevant features through iterative feature weighting with radial basis function networks. It proceeds with a set of feature weights to scale the data which are used to train a radial basis function network model. Then from the learned model, the feature weights are updated via one-step gradient descent. The updated feature weights are then fed back to build a new model. The procedure continues until we find a satisfactory model and the feature weights converge. Experimental results for some benchmark datasets show that the approach is efficient and effective for selecting relevant features for data modeling and classification tasks.

Interpretation of force and moment data is used to guide the searching phase of a circular peg-in-hole assembly. It is shown that the available measurements carry varying quality of information, ambiguous in much of the search space and relatively precise in a small region. By interpreting the history of sensory data acquired during an assembly attempt, reliable interpretation of the data is possible. A feature-based technique is described for interpreting sensory data. Experiments with robotic peg-in-hole assembly demonstrated a speed-up of nearly an order of magnitude relative to a blind search.

A new approach to dimension-reduction mapping of multidimensional pattern data is presented. The motivation for this work is to provide a computationally efficient method for visualizing large bodies of complex multidimensional data as a relatively "topologically correct" lower dimensional approximation. Examples of the use of this approach in obtaining meaningful two-dimensional (2-D) maps and comparisons with those obtained by the self-organizing map (SOM) and the neural-net implementation of Sammon's approach are also presented and discussed. In this method, the mapping equalizes and orthogonalizes the lower dimensional outputs by reducing the covariance matrix of the outputs to the form of a constant times the identity matrix. This new method is computationally efficient and "topologically correct" in interesting and useful ways.

In this paper a new method is suggested for learning and generalization with a general one-hidden layer feedforward neural network. This scheme encompasses the use of a linear combination of heterogeneous nodes having randomly prescribed parameter values. The learning of the parameters is realized through adaptive stochastic optimization using a generalization data set. The learning of the linear coefficients in the linear combination of nodes is achieved with a linear regression method using data from the training set. One node is learned at a time. The method allows for choosing the proper number of net nodes, and is computationally efficient. The method was tested on mathematical examples and real problems from materials science and technology.

This paper includes some comments and amendments of the above-mentioned paper by Igelnik et al. (1995). Subsequently, Theorem 1 in the above-mentioned paper has been revised. The significant change of the original theorem is the space of the thresholds in the hidden layer. The revised theorem says that the thresholds of hidden b/sub 0/, should be -w/sub 0//spl middot/y/sub 0/-u/sub 0/, where w/sub 0/=/spl alpha/w/spl circ//sub 0/; w/spl circ//sub 0/=(w/spl circ//sub 01/, /spl middot//spl middot//spl middot/, y/sub 0d/), and u/sub 0/ be independent and uniformly distributed in V/sup d/=[0; /spl Omega/]/spl times/[-/spl Omega/; /spl Omega/]/sup d-1/, I/sup d/, and [-2d/spl Omega/, 2d/spl Omega/], respectively. In reply, Igelnik et al. acknowledge that a factor of two was omitted in the statement of a trigonometric identity. However, the validity of the essential point of Theorem 1 is unaltered.

The subject matter of this paper is one of long-standing interest to the Pattern Recognition and Artificial Intelligence research communities, namely that of "feature extraction" for facilitating the task of classification or various other tasks. We show that internal representations of neural networks do not yield unique feature values but can provide the basis for facilitating a number of useful information management tasks, such as memorization, categorization, discovery, associative recall and others. These matters are illustrated with three sets of data, one of a benchmark nature, another of the nature of real-world sensor data, and a third set consisting of semiconductor crystal structure parameters.

Optical ellipsometry has been found to be a promising technique for monitoring process parameters, such as film composition and film thickness, of semiconductor wafers grown with molecular beam epitaxy. Whereas it is a straightforward task to calculate ellipsometry angles given the thickness of the film and the refractive indexes of the film and substrate, it is a difficult task to invert that mathematical relationship. However, the process must be inverted if the measured parameters are to be interpreted meaningfully in terms of film composition and film thickness. This paper reports on the use of neural-net computing for the inverse mapping of measured ellipsometry parameters. We used a functional-link net which is very efficient in function approximation. The advantage of using the net, however, is not only its speed, but also because some other net architecture characteristics allow us to perform the task in a holistic manner.

A theoretical justification for the random vector version of the functional-link (RVFL) net is presented in this paper, based on a general approach to adaptive function approximation. The approach consists of formulating a limit-integral representation of the function to be approximated and subsequently evaluating that integral with the Monte-Carlo method. Two main results are: (1) the RVFL is a universal approximator for continuous functions on bounded finite dimensional sets, and (2) the RVFL is an efficient universal approximator with the rate of approximation error convergence to zero of order O(C/ radicaln), where n is number of basis functions and with C independent of n. Similar results are also obtained for neural nets with hidden nodes implemented as products of univariate functions or radial basis functions. Some possible ways of enhancing the accuracy of multivariate function approximations are discussed.

Feasible approaches to the task of solving NP-complete problems usually entails the incorporation of heuristic procedures so as to increase the efficiency of the methods used. We propose a new technique, which incorporates the idea of simulated annealing into the practice of simulated evolution, in place of arbitrary heuristics. The proposed technique is called guided evolutionary simulated annealing (GESA). We report on the use of GESA approach primarily for combinatorial optimization. In addition, we report the case of function optimization, treating the task as a search problem. The traveling salesman problem is taken as a benchmark problem in the first case. Simulation results are reported. The results show that the GESA approach can discover a very good near optimum solution after examining an extremely small fraction of possible solutions. A very complicated function with many local minima is used in the second case. The results in both cases indicate that the GESA technique is a practicable method which yields consistent and good near optimal solutions, superior to simulated evolution.

Underwater acoustic transients can develop from a wide variety of sources. Accordingly, detection and classification of such transients by automated means can be exceedingly difficult. This paper describes a new approach to this problem based on adaptive pattern recognition employing neural networks and an alternative metric, the Hausdorff metric. The system uses self-organization to both generalize and provide rapid throughput while utilizing supervised learning for decision making, being based on a concept that temporally partitions acoustic transient signals, and as a result, studies their trajectories through power spectral density space. This method has exhibited encouraging results for a large set of simulated underwater transients contained in both quiet and noisy ocean environments, and requires from five to ten MFLOPS for the implementation described.

In this paper, we present a recurrent neural net technique to provide control actions for nonlinear dynamic systems. In most current neural net control approaches, two nets are usually required. One acts as a system emulator, and the other one is a controller network. Rather than using two nets, our system requires only one net which is the system emulator. In our proposed system, a neural net is used to learn the forward dynamics of the system, and the control signal is evolved from the output of the same net with use of an equation of motion. There is no need to learn the control law from another neural net, such as a system inverse net. The use of the proposed algorithm is illustrated with an example.>

The quadratic assignment problem, one of the classical NP-complete problems, is usually interpreted as a facility layout problem, of which the task is to assign facilities to locations in a manner so as to minimize a total cost function. The complexity of the problem has motivated the development of many approximation procedures. In this paper, the authors present a new approach to the quadratic assignment problem (QAP). The new technique, called guided evolutionary simulated annealing (GESA), is a parallel algorithm. It combines the ideas of simulated evolution and simulated annealing in a novel way. The authors demonstrate the technique using facility layout problems as examples. Simulation results for benchmark problems are reported. The results indicate that the GESA method outperforms other approximation methods.>

A case associative assembly planning system (CAAPS), which integrates neural computing techniques and rule-based systems has been developed. The neural network computing captures the designer's design concept and self-organizes similar experienced designs. The CBAPM (CLIPS-based assembly planning module), a component of CAAPS, generates a task-level assembly plan automatically. The design concept is expressed by a standard pattern format representing components' 3D geometry. A feature-based model translates the conceptual design into a preliminary boundary representation (B-rep). Based on a refinement of the B-rep assembly representation, assembly plans are generated for practical use in a single-robot assembly workcell. A feasible assembly plan that minimizes tool changes and subassembly reorientations is generated from the system. The CBCAPM presented draws input relationships directly from the conceptual design and the geometry of the assembly. At all stages of the design process the designer can consult the design cluster memory and plan cluster memory to see what "experience" knows of similar assemblies. Efficient use of prior experiences is emphasized.>

A brief overview is given of the nature of present day artificial neural net computing and the authors emphasize the theme that in this mode of computing, knowledge is not represented symbolically, but in the form of distributed processing and localized decision rules. The authors propose that in neural-net computing, the processing is the representation. In other words, the very nature of the processing encodes the knowledge. There is no place and no need for a separate body of global rules to be used by the network for inferencing. If rules exist at all, they are in the nature of local processing steps carried out at individual processors in response to stimuli from other neurons. The authors develop this theme together with a theme which is closely related to it. The second notion is that neural networks may also be thought of and implemented in terms of heterogeneous networks, rather than always or only in terms of massive arrays of identical elemental processors.>

A novel approach to the analysis of transient signals is proposed and demonstrated. This approach is based on the use of a neural net to estimate the future values of a transient time sequence. In the course of training the net, it learns a model of the process which is the source of the transient signal. This type of analysis is similar to the act of parsing in syntactic pattern recognition, and the act of synthesizing a model of the process is similar to grammar inference in syntactic pattern recognition, except that in the present case the signal values are numeric and are continuously valued. The approach is demonstrated with use of a nonlinear dynamic model as the signal generator. With this model, it is possible to move from simple signal environments to chaotic regions. Numerical results are presented to illustrate highly accurate predictive estimates of the signal source output

A neural control model based on learning of the system inverse is proposed. Learning control is a control method wherein experience gained from previous performance is automatically used to improve future performance. A learning scheme called the inverse transfer learning scheme is introduced. Compared to previous learning schemes, this scheme provides faster convergences to the minimum error state and reflects properties of highly coupled nonlinear dynamic systems. The scheme is applied to the pole-balancing control problem through computer simulation to demonstrate control capability.>

Pattern-recognition based methodology implemented in the architecture of artificial neural networks can be used to model knowledge-intensive feedback control systems. The procedure for development and practical design of a neural-net based control system is described and demonstrated by the example of a nonlinear hydraulic system. The neural-net control system has been trained to provide stabilizing controls and to detect and identify malfunctioning sensors. The results of computer simulations and experiments are presented to illustrate the proposed approach. They show that the neural-net control system is capable of maintaining any desired level of liquid in a tank, for tanks of various shapes; of stabilizing the system in the presence of large disturbances; of detecting and identifying defective level sensors; and of estimating parameters in nonlinear processes.>

Ordinal reasoning plays a major role in human cognition. This paper identifies an important class of classification problems of patterns taken from ordinal domains and presents efficient, incremental algorithms for learning the classification rules from examples. We show that by adopting a monotonicity assumption of the output with respect to the input, inconsistencies among examples can be easily detected and the number of possible classification rules substantially reduced. By adopting a conservative classification criterion, the required number of rules further decreases. The monotonicity and conservatism of the classification also enable the resolution of conflicts among inconsistent examples and the graceful handling of don't knows and don't cares during the learning and classification phases. Two typical examples in which the suggested classification model works well are given. The first example is taken from the financial domain and the second from machining.

The authors describe a method for reconstruction of three-dimensional visible and invisible opaque surfaces using moving shadows. An object whose shape is to be determined is placed on a reference surface. A beam of substantially parallel rays of light is projected at the object at a set of different angles relative to the reference surface. Using a camera which is placed above the reference surface, the shadows cast by the object for each angle are transferred to a computer. A three-dimensional binary level shadow diagram (3DBL shadowgram) is formed and analyzed. The shadowgram has some features which make the reconstruction very simple: a section of the 3DBL shadowgram, referred to as a 2DBL shadowgram, can be used to determine the heights of points of the object to be reconstructed. Further analysis of some curves of the shadowgram can be used for the partial reconstruction of invisible surfaces. A set of experimental results to test the effects of the threshold, camera resolution, and the number of pictures demonstrates the robustness and usefulness of the method.>

A method for segmenting three-dimensional overlapping surfaces is presented that is based on moving a light source in a horizontal plane relative to the surfaces to be segmented. Using a camera that is placed above the surfaces. The shadows cast by the surfaces at each light source angle are recorded and analyzed. The segmentation algorithm is simple and based on Boolean processing of the data. A set of experimental results demonstrates the robustness and usefulness of the method.>

In recent years, there has been considerable renewed interest in the analysis of eigenstructure of the estimated signal covariance matrix for spectrum estimation or, equivalently, beam forming. The computational task involved is generally very large. In this paper, we report on two methods developed for improving the computational efficiency after the eigenvectors are known. These two methods both rely on the use of a single vector rather than the entire set of all the eigenvectors. In other words, the Simplified Formula (SF) method, the single vector is the sum of the autocorrelation function vector of all the noise eigenvectors. In the other method, the Minimizing Magnitude (MM) method, the single vector is the autocorrelation function vector of a weighted combination of all the noise eigenvectors. Simulations show that both methods yield good results with a decrease in computational effect proportional to the number of noise eigenvectors.

In many signal processing tasks one is frequently faced with situations where a large system of linear equations with a toeplitz matrix, i.e. of the Yule-Walker equations form, needs to be solved. One of the most efficient methods of solving these equations is the Durbin's recursive procedure. This paper presents a pipeline architecture, with feedback, for implementing the Durbin's recursive algorithm in a more computationaly efficient manner. A speed-up time analysis is also provided which shows that the proposed pipeline implementation is four times faster than the conventional sequential implementation.

An experimental text-to-speech software system has been developed. The principle of the system is based on an initial-final model. Under this model, only 58 phonemes and semisyllables are needed for synthesizing unrestricted Chinese words. The standard Pin-Yin system is used for input through a conventional keyboard. Since tone information is an essential acoustic feature of each spoken Chinese word, four basic tonal variation patterns have been developed and are incorporated into the synthesis process. Some principal sandhi rules are also considered. For improving the intelligibility and quality of the output sound, mixed excitation is used for unaspirated fricatives. Also "zeros" of the spectrum are added into the vocal tract model when appropriate for nasals and some consonants.

In multifrequency ultrasound holographic imaging systems it should be possible in principle to use ARMA modeling to interpolate and extrapolate spatial arrays of detectors and to interpolate and extrapolate discrete frequencies used for frequency diversity. Our preliminary results indicate that this hypothesis is valid. However more explorations are required before general use can be recommended.

In spoken Chinese, pitch is an essential acoustic feature in addition to formants. Previous studies have reported on the overall structure of some features of Chinese Putonghua [1,2]. In this paper, some further aspects of these features are reported in detail. In particular, the details of the variation of pitch in connected words are reported, and a simple model useful for rationalizing the observed patterns is provided. Other topics covered include the formant structure of vowels, acoustic modelling of nasal sounds and guidelines for word segmentation in continuous speech.

Recently Cadzow demonstrated that under certain circumstances, an ARMA spectrum estimation procedure proposed by him yields results superior to those of Fourier Transform, Maximum Entropy and other standard methods. The procedure seems to be especially suitable for noisy environments and for revealing the presence of weak components in the presence of other components. We have investigated the performance of the technique for more general circumstances. Another matter investigated is the optimum order of the pole-zero model.