Approximation capabilities of hierarchical hybrid systems

This paper investigates the approximation capabilities of hierarchical hybrid systems, which are motivated by research in hierarchical fuzzy systems, hybrid intelligent systems, and modeling of model partly known systems. For a function (system) with known hierarchical structure (i.e., one that can be represented as a composition of some simpler and lower dimensional subsystems), it is shown that hierarchical hybrid systems have the structure approximation capability in the sense that such a hybrid approximation scheme can approximate both the overall system and all the subsystems to any desired degree of accuracy. For a function (system) with unknown hierarchical structure, Kolmogorov's theorem is used to construct the hierarchical structure of the given function (system). It is then shown that hierarchical hybrid systems are universal approximators.     

Increasing capabilities of geometric modelling systems

Geometric modelling is the basis for design and technological planning. The integration of these tasksis the goal of the Norwegian-German APS project (Advanced Production System). In this project, the geometric model is created as a module within a solid model that handles analytic and nonanlytic geometry. The module has two interfaces: to the object-oriented operational database and to applications via a geometry-definition interface that will be enlarged to become an application interface. The geometric module can be integrated into vendor environments which normally have two-dimensional capabilities. The functional capabilities are based in COMPAC and the GPM-sculptured surface system. Both systems are merged in such a way that the data structures are integrated and one database management system is used.     

On cryptographic protection of capabilities

A capability is an unforgeable, unstealable ticket which allows access to an object in a computer system or network. In a single computer, hardware mechanisms can be used to implement capabilities: in a network, there can be many different kinds of computers, some or all of which may not have the necessary hardware to implement capabilities. However, it is possible to implement capabilities in a network using cryptography. Capability-based protection using secret-key cryptography is currently being implemented in LLNL's Octopus Network. The protection algorithms being implemented are explained in deatail. Some hypothetical oporating system kernel calls and their operation are shown. Actual performance     

Embedding deductive capabilities in relational database systems

The basic idea of our work is to include deductive capabilities in a relational database system by fully using the operands of relational algebra and without using any theorem prover. We present a deductive database system where the user can formulate deduction laws as logical formulas, but which works on an underlying relational database system by fully using the data manipulation routines of such a system. Generally, it is not possible to express the deduction laws by means of expressions of relational algebra (see Ref. 1). In our system, a relational algebra expression is associated with every database deduction law. The result of its evaluation (with respect to an underlying database) is the set of all database entries that can be deduced from the database by applying the respective law once (Theorem 1). By successively applying all these relational expressions for all the database deduction laws it is possible to generate the whole extensional database (i.e. all tuples deducible from the database).     

A note on smooth approximation capabilities of fuzzy systems

Modeling and prediction in some systems requires the simultaneous approximation of mappings and their derivatives to a certain finite order. In this paper, universal approximation capabilities of fuzzy systems are extended to this situation, by showing the denseness of some general classes of fuzzy models in appropriate function spaces where distance between functions is defined in terms of their derivatives. Requirements are generally very mild, and are often limited to imposing sufficient differentiability conditions on the classes of fuzzy systems to be used. The cases covered by this paper include additive fuzzy systems with Gaussian membership functions and general additive fuzzy models with realistic membership functions. Some potential application fields are considered, including examples from economics and time series prediction     

On mapping systolic algorithms onto the hypercube

Consideration is given to the problem of mapping systolic array algorithms into efficient algorithms for a fixed-size hypercube architecture. The authors describe in detail several optimal implementations of algorithms given for one-way one- and two-dimensional systolic arrays. Since interprocessor communication is many times slower than local computation in parallel computers built to date, the problem of efficient communication is specifically addressed for these mappings. In order to validate the technique experimentally, five systolic algorithms were mapped in various ways onto a 64-node NCUBE/7 MIMD hypercube machine. The algorithms are for the following problems: the shuffle scheduling problem, finite impulse response filtering, linear context-free language recognition, matrix multiplication, and computing the Boolean transitive closure. Experimental evidence indicates that good performance is obtained for the mappings     

Extending the functional capabilities of NC systems for control over mechano-laser processing

The universal architecture of NC system to control mechano-laser machines tools is offered. The specific character of application of NC systems for various methods of laser processing is shown: laser sintering, engraving with a low frequency impulse emission. As an example of use of NC systems to control complex systems of the mechano-laser equipment the machine representing processing center, realizing hybrid technology is considered: machining in combination with the laser.     

A note on model-free regression capabilities of fuzzy systems.

Nonparametric estimation capabilities of fuzzy systems in stochastic environments are analyzed in this paper. By using ideas from sieve estimation, increasing sequences of fuzzy rule-based systems capable of consistently estimating arbitrary regression surfaces are constructed. Results include least squares learning of a mapping perturbed by additive random noise in a static-regression context. L1 (i.e., least absolute deviation) estimation is also studied, and the consistency of fuzzy rule-based sieve estimators for L1-optimal regression surfaces is shown, thus giving additional theoretical support to the robust filtering capabilities of fuzzy systems and their adequacy for modeling, prediction, and control of systems affected by impulsive noise.     

A systolic algorithm for solving dense linear systems

For an arbitrary n × n matrix A and an n × 1 column vector b, we present a systolic algorithm to solve the dense linear equations Ax = b. An important consideration is that the pivot row can be changed during the execution of our systolic algorithm. The computational model consists of n linear systolic arrays. For 1 ≤ i ≤ n, the i^th linear array is responsible to eliminate the i^th unknown variable x_i of x. This algorithm requires 4n time steps to solve the linear system. The elapsed time unit within a time step is independent of the problem size n. Since the structure of a PE is simple and the same type PE executes the identical instructions, it is very suitable for VLSI implementation. The design process and correctness proof are considered in detail. Moreover, this algorithm can detect whether A is singular or not.     

On the partitioning capabilities of feedforward neural networks with sigmoid nodes

In this letter, the capabilities of feedforward neural networks (FNNs) on the realization and approximation of functions of the form g: R(l) --> A, which partition the R(l) space into polyhedral sets, each one being assigned to one out of the c classes of A, are investigated. More specifically, a constructive proof is given for the fact that FNNs consisting of nodes having sigmoid output functions are capable of approximating any function g with arbitrary accuracy. Also, the capabilities of FNNs consisting of nodes having the hard limiter as output function are reviewed. In both cases, the two-class as well as the multiclass cases are considered.     

Solution of dense linear systems on an optimal systolic architecture

The paper presents an optimal systolic array architecture for rapid solution of dense systems of linear equations. The array solves a system of size n×n in 4n + 1 time units including I/0 time. Data communications are strictly local and the processing elements (PEs) are simple. The complete three-phase solution algorithm is executed on a single array, employing about 3n²/2 PEs without any need for costly inter-phase I/0. Due to a novel data steering mechanism, the three algorithmic phases are maximally overlapped. Design optimality is established using systolic precedence diagrams. It is also shown that merging the functions of two adjacent PEs into a single PE is possible resulting in maximal PE utilization. An interesting result regarding cascading phase-optimal arrays is obtained.     

Integrative systems: assessing requirements and capabilities forintra- and inter-organizational contexts

Of the several major functions of management (planning, control, etc.), integration remains the least well-elaborated in terms of its technical implications. It has been dealt with primarily as an aspect of formal organization theory where, naturally enough, the focus is on integrative requirements, deficiencies, and remedies that are essentially structural or configurational in nature. In contrast, the emphasis in these pages is instrumental, focused mainly on the relationship between technical requirements and technical capabilities. This paper is divided into four parts. The first part works through a generalized conceptual framework that will hopefully provide some meaningful insights into the origin and nature of integrative requirements. In the second part is proposed a four-part taxonomy of technically significant integrative modes. In the third part, arguments about the apparent reach and limits of currently available integrative instruments are presented. The final part suggests some ways in which implications drawn from the technical perspective might perhaps usefully complement integrative inquiries undertaken by management and transaction theorists     

On systolic arrays for complex matrix problems

We consider systolic arrays for matrix computations involving complex elements, and show that in certain circumstances the complex calculations can be decomposed into a number of real matrix sub-problems which can be used to good effect in reducing computation times and increasing array cell efficiency. Computations involving matrix vector, matrix product and matrix factorisation are examined. It was found that matrix vector and product calculation produce arrays which have e= 1 cell efficiency and the same computation time as their real counterparts, with only an increase in hardware related to the bandwidth of the systems.

Matrix factorisation is achieved by using 2?2 block factorisation requiring four times the hardware and is only twice as slow as the real version of the factorisation.     

On the capabilities of higher-order neurons: a radial basis function approach

Higher-order neurons with k monomials in n variables are shown to have Vapnik-Chervonenkis (VC) dimension at least nk + 1. This result supersedes the previously known lower bound obtained via k-term monotone disjunctive normal form (DNF) formulas. Moreover, it implies that the VC dimension of higher-order neurons with k monomials is strictly larger than the VC dimension of k-term monotone DNF. The result is achieved by introducing an exponential approach that employs gaussian radial basis function neural networks for obtaining classifications of points in terms of higher-order neurons.     

An empirical study of modeling self-management capabilities in autonomic systems using case-based reasoning

Autonomic systems promise to inject self-managing capabilities in software systems. The major objectives of autonomic computing are to minimize human intervention and to enable a seamless self-adaptive behavior in the software systems. To achieve self-managing behavior, various methods have been exploited in past. Case-based reasoning (CBR) is a problem solving paradigm of artificial intelligence which exploits past experience, stored in the form of problem–solution pairs. We have applied CBR based modeling approach to achieve autonomicity in software systems. The proposed algorithms have been described and CBR implementation on externalization and internalization architectures of autonomic systems using two case studies RUBiS and Autonomic Forest Fire Application (AFFA) have been shown. The study highlights the effect of 10 different similarity measures, the role of adaptation and the effect of changing nearest neighborhood cardinality for a CBR solution cycle in autonomic managers. The results presented in this paper show that the proposed CBR based autonomic model exhibits 90–98% accuracy in diagnosing the problem and planning the solution.     

Supply chain management information systems capabilities. An exploratory study of electronics manufacturers

Supply Chain Management Information Systems (SCM IS) play an increasingly critical role in the ability of firms to reduce costs and increase the responsiveness of their supply chain. This paper develops an empirically supported model of the organizational capabilities enabled by SCM IS. The model integrates and enriches theoretical and empirical studies of competitive strategy, supply chain management, and interorganizational information systems. Evidence from an exploratory case study of three large firms in the electronics manufacturing industry is examined to build a better-supported theory of SCM IS capabilities. The findings suggest the organizational capabilities enabled by SCM IS can be conceptualized as the level of support provided for: operational efficiency; operational flexibility; internal planning and analysis; and external planning and analysis. The theoretical model furthers an understanding of SCM IS capabilities and is sufficiently developed to permit operationalization for future studies evaluating the effectiveness of SCM IS.     

Modular fuzzy-reinforcement learning approach with internal model capabilities for multiagent systems.

To date, many researchers have proposed various methods to improve the learning ability in multiagent systems. However, most of these studies are not appropriate to more complex multiagent learning problems because the state space of each learning agent grows exponentially in terms of the number of partners present in the environment. Modeling other learning agents present in the domain as part of the state of the environment is not a realistic approach. In this paper, we combine advantages of the modular approach, fuzzy logic and the internal model in a single novel multiagent system architecture. The architecture is based on a fuzzy modular approach whose rule base is partitioned into several different modules. Each module deals with a particular agent in the environment and maps the input fuzzy sets to the action Q-values; these represent the state space of each learning module and the action space, respectively. Each module also uses an internal model table to estimate actions of the other agents. Finally, we investigate the integration of a parallel update method with the proposed architecture. Experimental results obtained on two different environments of a well-known pursuit domain show the effectiveness and robustness of the proposed multiagent architecture and learning approach.     

Comparing the online learning capabilities of Gaussian ARTMAP and Fuzzy ARTMAP for building energy management systems

Recently, there has been a growing interest in the application of Fuzzy ARTMAP for use in building energy management systems or EMS. However, a number of papers have indicated that there are important weaknesses to the Fuzzy ARTMAP approach, such as sensitivity to noisy data and category proliferation. Gaussian ARTMAP was developed to help overcome these weaknesses, raising the question of whether Gaussian ARTMAP could be a more effective approach for building energy management systems? This paper aims to answer this question. In particular, our results show that Gaussian ARTMAP not only has the capability to address the weaknesses of Fuzzy ARTMAP but, by doing this, provides better and more efficient EMS controls with online learning capabilities.     

Entrepreneurial capabilities

The aim of this article is to analyse entrepreneurship from an action research perspective. What is entrepreneurship about? Which are the fundamental capabilities and processes of entrepreneurship? To answer these questions the article includes a case study of a Danish entrepreneur and his networks. Finally, the article discusses how more long term action research methods could be integrated into the entrepreneurial processes and the possible impacts of such an implementation.