Fusion of soft computing and hard computing in industrial applications: an overview
01 May 2002-Vol. 32, Iss: 2, pp 72-79
TL;DR: An overview of applications in which the fusion of soft computing and hard computing has provided innovative solutions for challenging real-world problems is presented.
Abstract: 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.
Citations
More filters
TL;DR: It is shown that these proposed Bio-inspired Imprecise Computational blocks (BICs) can be exploited to efficiently implement a three-layer face recognition neural network and the hardware defuzzification block of a fuzzy processor.
Abstract: The conventional digital hardware computational blocks with different structures are designed to compute the precise results of the assigned calculations. The main contribution of our proposed Bio-inspired Imprecise Computational blocks (BICs) is that they are designed to provide an applicable estimation of the result instead of its precise value at a lower cost. These novel structures are more efficient in terms of area, speed, and power consumption with respect to their precise rivals. Complete descriptions of sample BIC adder and multiplier structures as well as their error behaviors and synthesis results are introduced in this paper. It is then shown that these BIC structures can be exploited to efficiently implement a three-layer face recognition neural network and the hardware defuzzification block of a fuzzy processor.
458 citations
Cites background or methods from "Fusion of soft computing and hard c..."
...2027626 achieve robustness, tractability, and low cost [1]....
[...]
...that the soft-computing methodologies have already been advantageous in numerous areas [1]....
[...]
...These rigorous techniques that are known as hard-computing are normally used to solve a category of problems with similar properties such as high precision, predictability, and strict analysis [1]....
[...]
TL;DR: With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture.
242 citations
Cites background from "Fusion of soft computing and hard c..."
...Pearl (1988)made an important surveyon this topicwithanemphasisonBayesiannetworks....
[...]
...The fusion of them has great potential for developing high-performance, cost-effective, and reliable computing schemes that provide innovative solutions to problems (Ovaska et al., 2002)....
[...]
TL;DR: In this article, the authors developed a new algorithm for maximum power point tracking (MPPT) in large PV systems under partial shading conditions (PSC), which combines the use of particle swarm optimization (PSO) for MPPT during the initial stages of tracking and then employs the traditional perturb and observe (PO) method at the final stages.
177 citations
TL;DR: An overview of the advanced control strategies for heating, ventilation, air-conditioning, and refrigeration (HVAC&R) is presented, which focuses on hard-computing or control techniques, such as proportional-integral-derivative, optimal, nonlinear, adaptive, and robust; and on the fusion or hybrid of hard- and soft-control techniques.
Abstract: A chronological overview of the advanced control strategies for heating, ventilation, air-conditioning, and refrigeration (HVACR soft-computing or control techniques, such as neural networks, fuzzy logic, genetic algorithms; and on the fusion or hybrid of hard- and soft-control techniques. Thus, it is to be noted that the terminology “hard” and “soft” computing/control has nothing to do with the “hardware” and “software” that is being generally used. Part I of a two-part series focuses on hard-control strategies, and Part II focuses on soft- and fusion-control in addition to some future directions in HVAC&R research. This overview is not intended to be an exhaustive survey on this topic, and any omission of other works is purely unintentional.
75 citations
Cites background or methods from "Fusion of soft computing and hard c..."
...…Uhrig 1997; Nguyen et al. 2003; Karray and De Silva 2004; Konar 2005; Kasabov 2007; Sumathi et al. 2008); and 3. hybrid control resulting from the fusion of SC and HC to achieve a better performance (Ovaska et al. 2002; Tettamanzi and Tomassini 2001; Konar 2005; Kasabov 2007; Sumathi et al. 2008)....
[...]
...hybrid control resulting from the fusion of SC and HC to achieve a better performance (Ovaska et al. 2002; Tettamanzi and Tomassini 2001; Konar 2005; Kasabov 2007; Sumathi et al. 2008)....
[...]
...It is to be noted that the new terminology, the “hard” in HC and “soft” in SC, has been used recently in the control systems community (Ovaska et al. 2002; Karray and De Silva 2004) and has nothing to do with the “hardware” and “software” that is generally used....
[...]
01 Feb 2007
TL;DR: To develop a design that satisfies the ‘ideals’ of a satisfactory prosthetic, an interdisciplinary team of biomedical and tissue engineers, and biomaterial and biomedical scientists is needed to work together holistically and synergistically.
Abstract: Although among designs of prosthetics there have been some successes in the design of functional robotic implants, there remain many issues and challenges concerned with the failure to meet...
61 citations
References
More filters
Book•
[...]
01 Aug 1996
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Abstract: A fuzzy set is a class of objects with a continuum of grades of membership. Such a set is characterized by a membership (characteristic) function which assigns to each object a grade of membership ranging between zero and one. The notions of inclusion, union, intersection, complement, relation, convexity, etc., are extended to such sets, and various properties of these notions in the context of fuzzy sets are established. In particular, a separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
52,705 citations
Book•
01 Jan 1970TL;DR: A reverse-flow technique is described for the solution of a functional equation arising in connection with a decision process in which the termination time is defined implicitly by the condition that the process stops when the system under control enters a specified set of states in its state space.
Abstract: By decision-making in a fuzzy environment is meant a decision process in which the goals and/or the constraints, but not necessarily the system under control, are fuzzy in nature. This means that the goals and/or the constraints constitute classes of alternatives whose boundaries are not sharply defined.
An example of a fuzzy constraint is: “The cost of A should not be substantially higher than α,” where α is a specified constant. Similarly, an example of a fuzzy goal is: “x should be in the vicinity of x0,” where x0 is a constant. The italicized words are the sources of fuzziness in these examples.
Fuzzy goals and fuzzy constraints can be defined precisely as fuzzy sets in the space of alternatives. A fuzzy decision, then, may be viewed as an intersection of the given goals and constraints. A maximizing decision is defined as a point in the space of alternatives at which the membership function of a fuzzy decision attains its maximum value.
The use of these concepts is illustrated by examples involving multistage decision processes in which the system under control is either deterministic or stochastic. By using dynamic programming, the determination of a maximizing decision is reduced to the solution of a system of functional equations. A reverse-flow technique is described for the solution of a functional equation arising in connection with a decision process in which the termination time is defined implicitly by the condition that the process stops when the system under control enters a specified set of states in its state space.
6,919 citations
"Fusion of soft computing and hard c..." refers background in this paper
...Already in 1970, Bellman and Zadeh introduced a fuzzy set concept into the reverse-flow-based dynamic programming method to solve multistage decision-making problems in a fuzzy environment [ 8 ]....
[...]
01 Sep 1990
TL;DR: Regularization networks are mathematically related to the radial basis functions, mainly used for strict interpolation tasks as mentioned in this paper, and two extensions of the regularization approach are presented, along with the approach's corrections to splines, regularization, Bayes formulation, and clustering.
Abstract: The problem of the approximation of nonlinear mapping, (especially continuous mappings) is considered. Regularization theory and a theoretical framework for approximation (based on regularization techniques) that leads to a class of three-layer networks called regularization networks are discussed. Regularization networks are mathematically related to the radial basis functions, mainly used for strict interpolation tasks. Learning as approximation and learning as hypersurface reconstruction are discussed. Two extensions of the regularization approach are presented, along with the approach's corrections to splines, regularization, Bayes formulation, and clustering. The theory of regularization networks is generalized to a formulation that includes task-dependent clustering and dimensionality reduction. Applications of regularization networks are discussed. >
3,595 citations
"Fusion of soft computing and hard c..." refers background in this paper
...RBF networks are particularly suitable for approximation of continuous mappings [ 17 ], and their excellent generalization capability makes it possible to provide human-like perception for solving the demanding problem efficiently....
[...]
01 Oct 1996
TL;DR: The self-organizing map method, which converts complex, nonlinear statistical relationships between high-dimensional data into simple geometric relationships on a low-dimensional display, can be utilized for many tasks: reduction of the amount of training data, speeding up learning nonlinear interpolation and extrapolation, generalization, and effective compression of information for its transmission.
Abstract: The self-organizing map (SOM) method is a new, powerful software tool for the visualization of high-dimensional data. It converts complex, nonlinear statistical relationships between high-dimensional data into simple geometric relationships on a low-dimensional display. As it thereby compresses information while preserving the most important topological and metric relationships of the primary data elements on the display, it may also be thought to produce some kind of abstractions. The term self-organizing map signifies a class of mappings defined by error-theoretic considerations. In practice they result in certain unsupervised, competitive learning processes, computed by simple-looking SOM algorithms. Many industries have found the SOM-based software tools useful. The most important property of the SOM, orderliness of the input-output mapping, can be utilized for many tasks: reduction of the amount of training data, speeding up learning nonlinear interpolation and extrapolation, generalization, and effective compression of information for its transmission.
845 citations
"Fusion of soft computing and hard c..." refers background in this paper
...The self-organizing map (SOM) of Kohonen [ 30 ] could provide an interesting base for further developments in this particular application....
[...]
02 Oct 1994
TL;DR: The application of neural networks for estimation of feedback signals in induction motor drive systems is explored and the neural network estimator has the advantages of faster execution speed, harmonic ripple immunity and fault tolerance characteristics compared to the DSP-based estimator.
Abstract: Neural networks are showing promise for application in power electronics and motion control systems. So far, they have been applied for a few cases, mainly in the control of converters and drives, but their application in estimation is practically new. The purpose of this paper is to demonstrate that such a technology can be applied for estimation of feedback signals in an induction motor drive with some distinct advantages when compared to DSP based implementation. A feedforward neural network receives the machine terminal signals at the input and calculates flux, torque, and unit vectors (cos /spl theta//sub e/ and sin /spl theta//sub e/) at the output which are then used in the control of a direct vector-controlled drive system. The three-layer network has been trained extensively by Neural Works Professional II/Plus program to emulate the DSP-based computational characteristics. The performance of the estimator is good and is comparable to that of DSP-based estimation. The system has been operated in the wide torque and speed regions independently with a DSP-based estimator and a neural network-based estimator, and are shown to have comparable performance. The neural network estimator has the advantages of faster execution speed, harmonic ripple immunity, and fault tolerance characteristics compared to DSP-based estimator. >
190 citations
"Fusion of soft computing and hard c..." refers background in this paper
...In [ 13 ], a neural network-based estimator of feedback signals for a vector controlled induction motor drive was proposed by Simoes and Bose....
[...]