An adaptive, robust, and computationally efficient disturbance reduction method for line-frequency zero-crossing detectors is proposed. Our adaptive system consists of a fixed finite-impulse response filter block and two multiplicative general parameters. The predictive filter is able to handle wide variations in the line frequency without harmfully adapting to the harmonic components.

Adaptive filtering using multiplicative general parameters for zero-crossing detection

Soft computing (SC) is an emerging collection of methodologies which aims to exploit tolerance for imprecision, uncertainty, and partial truth to achieve robustness, tractability, and low total cost. It differs from conventional hard computing (HC) in the sense that, unlike hard computing, it is strongly based on intuition or subjectivity. Therefore, soft computing provides an attractive opportunity to represent the ambiguity in human thinking with real life uncertainty. Fuzzy logic (FL), neural networks (NN), and genetic algorithms (GA) are the core methodologies of soft computing. However, FL, NN, and GA should not be viewed as competing with each other, but synergistic and complementary instead. Considering the number of available journal and conference papers on various combinations of these three methods, it is easy to conclude that the fusion of individual soft computing methodologies has already been advantageous in numerous applications. On the other hand, hard computing solutions are usually more straightforward to analyze; their behavior and stability are more predictable; and, the computational burden of algorithms is typically either low or moderate. These characteristics. are particularly important in real-time applications. Thus, it is natural to see SC and HC as potentially complementary methodologies. Novel combinations of different methods are needed when developing high-performance, cost-effective, and safe products for the demanding global market. We present an overview of applications in which the fusion of soft computing and hard computing has provided innovative solutions for challenging real-world problems. A carefully selected list of references is considered with evaluative discussions and conclusions.

Fusion of soft computing and hard computing in industrial applications: an overview

By taking advantage of fuzzy systems and neural networks, a fuzzy-neural network with a general parameter (GP) learning algorithm and heuristic model structure determination is proposed in this paper. Our network model is based on the Gaussian radial basis function network (RBFN). We use the flexible GP approach both for initializing the off-line training algorithm and fine-tuning the nonlinear model efficiently in online operation. A modification of the robust unbiasedness criterion using distorter (UCD) is utilized for selecting the structural parameters of this adaptive model. The UCD approach provides the desired modeling accuracy and avoids the risk of over-fitting. In order to illustrate the operation of the proposed modeling scheme, it is experimentally applied to a fault detection application.

Fuzzy neural network with general parameter adaptation for modeling of nonlinear time-series

We introduce an evolutionary-programming-based method for designing robust and computationally efficient adaptive bandpass filters. These predictive filters are optimized for generating current references in active power filters (APFs). The accuracy (phase/amplitude) of the reference current is crucial in current-injection-type systems, because it directly affects the harmonics reduction ability of the APF. Our digital filtering approach has the following advantages: selective bandpass response, efficient attenuation of specific harmonic components, capability to handle typical frequency alteration, small number of multiplications, and structural simplicity. In addition, practically no prior knowledge of the electricity distribution network and its loading characteristics is needed for designing the current reference generator. In an illustrative example, the total harmonic distortion of an artificial current waveform was reduced from 36.7% to less than 3.7% within the line frequency range 49-51 Hz. The proposed scheme is a combination of the hard-computing (HC)-type multiplicative general parameter method and evolutionary programming that, on the other hand, is a constituent of soft computing (SC). Such open-minded fusion thinking is emerging among researchers and engineers, and it can potentially lead to efficient combinations of HC and SC methodologies*both on the algorithm level and on the system level.

Evolutionary-programming-based optimization of reduced-rank adaptive filters for reference generation in active power filters

This doctoral thesis consists of an introductory part and eight appended publications, which deal with hardware–based reprogrammability in algorithm acceleration with a specific emphasis on the possibilities offered by modern large–scale Field Programmable Gate Arrays (FPGAs) in computationally demanding applications. The historical evolution of both the theoretical and technological paths culminating in the introduction of reprogrammable logic devices is first outlined. This is followed by defining the commonly used terms in the thesis. The reprogrammable logic market is surveyed, and the architectural structures and the technological reasonings behind them are described in detail. As reprogrammable logic lies between Application Specific Integrated Circuits (ASICs) and general–purpose microprocessors in the implementation spectrum of electronics systems, special attention has been paid to differentiate these three implementation approaches. This has been done to emphasize, that reprogrammable logic offers much more than just a low–volume replacement for ASICs. Design systems for reprogrammable logic are investigated, as the learning curve associated with them is the main hurdle for software–oriented designers for using reprogrammable logic devices. The theoretically important topic of partial reprogrammability is described in detail, but it is concluded, that the practical problems in designing viable development platforms for partially reprogrammable systems will hinder its wide–spread adoption. The main technical, design–oriented, and economic applicability factors of reprogrammable logic are laid out. The main advantages of reprogrammable logic are their suitability for fine–grained bit–level parallelizable computing with a short time–to–market and low upfront costs. It is also concluded, that the main opportunities for reprogrammable logic lie in the potential of high–level design systems, and the ever–growing ASIC design gap. On the other hand, most power–conscious mass–market portable products do not seem to offer major new market potential

A.A. Ashimov

Papers

Identification of High Dimensional System by the General Parameter Method