Surface ozone variability in the urban and nearby rural locations of tropical India

Surface ozone variability at Pune (an urban location) and nearby rural locations has been studied using KI solution chemical method. The measurement showed well-marked diurnal cycle of ozone concentration with minimum at sunrise and maximum at noon hours. Simultaneous measurements of humidity and temperature along with ozone suggest that the ozone concentration is directly proportional to temperature and inversely proportional to humidity. The averaged diurnal variation of ozone during different months and two seasons, viz. winter and spring show high ozone concentration over the rural locations than the urban location. Higher ozone concentration at the rural locations may be due to slower titration of ozone by nitric oxide in the evening hours

Emotional Fuzzy Sliding-Mode Control for Unknown Nonlinear Systems

[[abstract]]The brain emotional learning model can be implemented with a simple hardware and processor; however, the learning model cannot model the qualitative aspects of human knowledge. To solve this problem, a fuzzy-based emotional learning model (FELM) with structure and parameter learning is proposed. The membership functions and fuzzy rules can be learned through the derived learning scheme. Further, an emotional fuzzy sliding-mode control (EFSMC) system, which does not need the plant model, is proposed for unknown nonlinear systems. The EFSMC system is applied to an inverted pendulum and a chaotic synchronization. The simulation results with the use of EFSMC system demonstrate the feasibility of FELM learning procedure. The main contributions of this paper are (1) the FELM varies its structure dynamically with a simple computation; (2) the parameter learning imitates the role of emotions in mammalians brain; (3) by combining the advantage of nonsingular terminal sliding-mode control, the EFSMC system provides very high precision and finite-time control performance; (4) the system analysis is given in the sense of the gradient descent method.[[notice]]補正完