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.

Bio-Inspired Imprecise Computational Blocks for Efficient VLSI Implementation of Soft-Computing Applications

Adaptive neural network based fuzzy inference system (ANFIS) is an intelligent neuro-fuzzy technique used for modelling and control of ill-defined and uncertain systems. ANFIS is based on the input-output data pairs of the system under consideration. The size of the input-output data set is very crucial when the data available is very less and the generation of data is a costly affair. Under such circumstances, optimization in the number of data used for learning is of prime concern. In this paper, we have proposed an ANFIS based system modelling where the number of data pairs employed for training is minimized by application of an engineering statistical technique called full factorial design. Our proposed method is experimentally validated by applying it to the benchmark Box and Jenkins gas furnace data and a data set collected from a thermal power plant of the North Eastern Electric Power Corporation (NEEPCO) Limited. By employing our proposed method the number of data required for learning in the ANFIS network could be significantly reduced and thereby computation time as well as computation complexity is remarkably reduced. The results obtained by applying our proposed method are compared with those obtained by using conventional ANFIS network. It was found that our model compares favourably well with conventional ANFIS model.

A novel approach for ANFIS modelling based on full factorial design

Artificial bee colony algorithm (ABC), which is inspired by the foraging behavior of honey bee swarm, is a biological-inspired optimization. It shows more effective than genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). However, ABC is good at exploration but poor at exploitation, and its convergence speed is also an issue in some cases. For these insufficiencies, we propose an improved ABC algorithm called I-ABC. In I-ABC, the best-so-far solution, inertia weight and acceleration coefficients are introduced to modify the search process. Inertia weight and acceleration coefficients are defined as functions of the fitness. In addition, to further balance search processes, the modification forms of the employed bees and the onlooker ones are different in the second acceleration coefficient. Experiments show that, for most functions, the I-ABC has a faster convergence speed and better performances than each of ABC and the gbest-guided ABC (GABC). But I-ABC could not still substantially achieve the best solution for all optimization problems. In a few cases, it could not find better results than ABC or GABC. In order to inherit the bright sides of ABC, GABC and I-ABC, a high-efficiency hybrid ABC algorithm, which is called PS-ABC, is proposed. PS-ABC owns the abilities of prediction and selection. Results show that PS-ABC has a faster convergence speed like I-ABC and better search ability than other relevant methods for almost all functions.

Development and investigation of efficient artificial bee colony algorithm for numerical function optimization

https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=3087&context=usdaarsfacpub

Review: Development of soft computing and applications in agricultural and biological engineering

Methodology for large-scale systems

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

The design of control systems for large-scale and complex industrial plants involves numerous trade-off problems, such as costs, quality, environmental impact, safety, reliability, accuracy, and robustness. Some of these parameters are even conflicting. Thus, the use of a multidiscipline approach is suggested to satisfy these requirements in an acceptable and well-balanced manner, and a fusion of soft computing and hard computing appears to be a natural and practical choice. Although the state-of-the-art soft computing technology has distinguished features, the use of soft computing technology would be ineffective, and may be doomed to fail, if it is improperly fused with conventional hard computing technology and control processes. Proper fusion is key to success, and a general model of fusion is worth examining. In this paper, through a survey of published literature, a general model of fusion is shown at the system level as well as at the algorithm level. In the system level, soft computing is applied to the upper level in a hierarchical control system, performing human-like tasks, such as forecasting and scheduling, or applied to ill-defined process models for carrying out intelligent control. Hard computing is used at the middle or lower control level for well-defined process models, carrying out coordinate control tasks while maintaining a high level of accurate and safety control. In the case of fusion at the algorithm level, this paper will discuss several types of tasks, such as scheduling and control.

Fusion of soft computing and hard computing for large-scale plants: a general model

Soft computing (SC) and hard computing (HC) methodologies are fused together successfully in numerous industrial applications. The principal aim is to develop computationally intelligent hybrid systems that are straightforward to analyze, with highly predictable behavior and stability, and with computational burden that is no more than moderate. All these goals are particularly important in embedded real-time applications. This paper is intended to clarify the present vagueness related to the fusion of SC and HC methodologies. We classify the different fusion schemes to 12 core categories and six supplementary categories, and discuss the characteristic features of SC and HC constituents in practical fusion implementations. The emerging fusion approach offers a natural evolution path from pure hard computing toward dominating soft computing

Fusion of soft computing and hard computing: computational structures and characteristic features

Soft computing (SC) is an emerging collection of methodologies, which aim 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 uses intuition or subjectivity. Therefore, soft computing provides an attractive opportunity to represent ambiguity in human thinking with the 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, as emphasized by Dr. Zadeh . Considering the available literature, it is easy to conclude that the fusion of individual soft computing methodologies has been advantageous in numerous applications. In this paper, we give a review of applications where the fusion of soft computing and hard computing has provided innovative solutions for demanding real-world problems. A representative list of references is provided with evaluative discussions and conclusions.

Fusion of soft computing and hard computing techniques: a review of applications

Soft computing (SC) and hard computing (HC) methodologies are fused together in numerous industrial and other applications. The aim is typically to develop intelligent systems that would be straightforward to analyze, their behavior and stability could be predictable, and the computational burden would be no more than moderate. All these goals are particularly important in embedded real-time applications. This paper clarifies the vagueness related to the fusion of SC and HC methodologies that slows down the acceptance of soft computing outside the computational intelligence community. We categorize the different fusion topologies to 12 core classes and 6 supplementary classes, and discuss the characteristic features of the SC and HC constituents in practical hybrid implementations.

Akimoto Kamiya

Papers

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

Fusion of soft computing and hard computing for large-scale plants: a general model

Fusion of soft computing and hard computing: computational structures and characteristic features

Fusion of soft computing and hard computing techniques: a review of applications

Classification of fusion topologies in hybrid soft computing and hard computing systems