Soft computing

About: Soft computing is a research topic. Over the lifetime, 6710 publications have been published within this topic receiving 118508 citations.

On the numerical solutions of stochastic optimization problem

Abstract: In this paper we discuss the role of soft computing (SC) in optimization problems. We point out that SC is closely related to computational intelligence. Each aims at complementing the limitations of many conventional techniques.

A comprehensive review on iris image-based biometric system

Abstract: Iris image-based biometric systems are commonly used in applications that demand security, authentication, recognition and faster login access. In solving these real-time problems, the impact of soft computing techniques which employ cognitive skills is very high. Although this system has been commercialized, the scope for improvement is still plenty. This paper introduces the reader to different segments of an iris recognition system and reviews the techniques involved with each segment. It reports on how research articles validate the robustness of an iris-based recognition system. As these systems are fallible, it also shows the vulnerabilities associated with each segment and provides insights to develop much better intelligent and robust techniques which will make the system more accurate. This paper also shows that in spite of versatility of soft computing techniques, it is not fully exploited for iris recognition systems. The present challenges and directions for future research are also discussed.

Fusion of Modified Bat Algorithm Soft Computing and Dynamic Model Hard Computing to Online Self-Adaptive Fuzzy Control of Autonomous Mobile Robots

Abstract: This paper presents a fusion methodology of modified bat algorithm (BA) soft computing and dynamic model hard computing to online self-adaptive fuzzy control of autonomous mobile robots in a field-programmable gate array (FPGA) chip. This fusion approach, called fusion of BA fuzzy control (FBAFC), gains the benefits of BA, online fuzzy control, dynamic model, Taguchi method, and FPGA technique. The FPGA realization of the proposed FBAFC fusion method is more effective in practice for challenging real-world embedded applications by using system-on-a-programmable-chip (SoPC) technology. Experimental results and comparative works are conducted to illustrate the merits of the proposed SoPC-based FBAFC optimal controller over other existing methods for mobile robots.

Towards Advanced Data Analysis by Combining Soft Computing and Statistics

Abstract: Soft computing, as an engineering science, and statistics, as a classical branch of mathematics, emphasize different aspects of data analysis. Soft computing focuses on obtaining working solutions quickly, accepting approximations and unconventional approaches. Its strength lies in its flexibility to create models that suit the needs arising in applications. In addition, it emphasizes the need for intuitive and interpretable models, which are tolerant to imprecision and uncertainty. Statistics is more rigorous and focuses on establishing objective conclusions based on experimental data by analyzing the possible situations and their (relative) likelihood. It emphasizes the need for mathematical methods and tools to assess solutions and guarantee performance. Combining the two fields enhances the robustness and generalizability of data analysis methods, while preserving the flexibility to solve real-world problems efficiently and intuitively.

Proposing of a new soft computing-based model to predict peak particle velocity induced by blasting

Abstract: Estimation of ground vibration induced by blasting operations is an important task to control the safety issues at the surface mines and civil projects. By reviewing the previous studies, some empirical and soft computing models have been proposed to estimate blast-induced ground vibrations. The main goal of this research is to propose a new predictive model in the field of ground vibration estimation. For this aim, the group method of data handling (GMDH) model which is a type of neural network, is proposed with respect to input parameters including the stemming length, powder factor, burden to spacing ratio, distance from the blast-face, blast-hole depth and maximum charge per delay. Also, the peak particle velocity, as the most common descriptor for evaluating the ground vibration, was selected as the output. The required datasets were collected from a quarry in Penang, Malaysia, using 102 blasting operations. Several criteria such as root mean square error (RMSE) and coefficient of determination (R2) were utilized to determine the reliability of the GMDH. Based on the obtained results, the GMDH forecasting technique with R2 of 0.911 and RMSE of 0.889 can be presented as a powerful technique in predicting the blast-induced ground vibration.