Grain Security Risk Level Prediction Using ANFIS

Kadir, Muhd Khairulzaman Abdul; Hines, Evor L.; Arof, Saharul; Illiescu, Daciana; Leeson, Mark S.; Dowler, Elizabeth; Collier, Rosemary; Napier, Richard; Qaddoum, Kefaya; Ghafari, Reza

doi:10.1109/cimsim.2011.27

Cited by 2 publications

(5 citation statements)

References 7 publications

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“…with parameters μ = 0.95 and t t r t i =ˆ| 15 . Then, by Example 1,ˆ| t t i t i = = 15 18 and te = 17. The simulated results are shown in Fig.…”

Section: Discussion On the Case Tf < +∞mentioning

confidence: 98%

“…Then, with respect to the continuous differential property of rν(s) in the interval (0, +∞) and Role Theorem, there should be rν(s) ≡ rν(0) in the interval [0, smin]. Applying this result to(15), one gets that f s p e p s s ∈ [0, smin], which shows m = 1. Thus, the monotonous property of rν(s) has been verified.…”

mentioning

confidence: 90%

“…In this paper, we study the modeling, forecasting, and control for stochastic systems with the so-called prospective strong intervention (PSI). The concept of PSI comes from practical backgrounds in economy [2,25], finance [10], industry [17,23,24,21], and agriculture [15,16], and it has two meanings. One is that the intervention will most likely occur in the near future and will last a period of time, even to infinity, thereby being distinguishable from impulsive disturbance.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On Prediction and Control of Discrete‐Time First‐Order Linear Stochastic Systems with Prospective Strong Intervention

Ru-dong¹

2014

Asian Journal of Control

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This paper is devoted to studying the issue of modeling, forecasting, and optimal feedback control of discrete-time first-order linear stochastic systems with prospective strong intervention (PSI). First, one type of PSI is formulated. Then, one class of induced mechanisms of PSI is characterized, and, with respect to the induced mechanisms, two multi-step forecasting methods for estimating the occurrence time and terminal time of PSI are discussed. Based on these fundamental results, the problem of optimal control with respect to an improved minimum variance performance index is discussed. Finally, the effectiveness and advantages of the proposed control strategy and approach are verified and demonstrated through numerical simulations.

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“…with parameters μ = 0.95 and t t r t i =ˆ| 15 . Then, by Example 1,ˆ| t t i t i = = 15 18 and te = 17. The simulated results are shown in Fig.…”

Section: Discussion On the Case Tf < +∞mentioning

confidence: 98%

mentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

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On Prediction and Control of Discrete‐Time First‐Order Linear Stochastic Systems with Prospective Strong Intervention

Ru-dong¹

2014

Asian Journal of Control

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“…This is contradictory to (14), that is, if p k < p lim , then p k+1 < p lim . In the same way, one can verify that p k+1 > p lim if p k > p lim .…”

Section: The Monotonic Property Ofmentioning

confidence: 90%

“…The study of modeling, forecasting and control for systems with prospective strong intervention (PSI) was initiated in [11], and it was pointed out that the concept of PSI came from practical backgrounds in economics [2,20], finance [9], industry [16,19], and agriculture [14,15]. The issue is distinctly different from the current research concerning disturbance or intervention to systems in both idea and content [3][4][5][6]8,10,12,13,17,18,20].…”

Section: Introductionmentioning

confidence: 99%

On Constrained MMVC of Discrete‐Time First‐Order Linear Stochastic Systems with PSI I: The Critically Stable Case

Ru-dong¹,

Chen²

2014

Asian Journal of Control

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This paper is devoted to the study of the modified minimal variance control (MMVC) of discrete-time first-order linear critically stable stochastic systems with prospective strong intervention (PSI) and control input constraints. Due to different evolutionary characteristics of systems with PSI, that is, the two modes of tending to infinity and having bounded oscillations, the discrete-time first-order linear critically stable systems can be partitioned into two types regarding the signs of a key system parameter a. A necessary and sufficient condition for the state mean convergence of a system with a = 1 is derived and the corresponding design of MMVC is formulated. For the critical stable system with a = −1, its oscillation amplitudes of state means can be effectively suppressed or the means can converge under control. Finally, the effectiveness and advantages of the proposed control strategies comparing with MVC are confirmed by numerical simulations.

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Grain Security Risk Level Prediction Using ANFIS

Cited by 2 publications

References 7 publications

On Prediction and Control of Discrete‐Time First‐Order Linear Stochastic Systems with Prospective Strong Intervention

On Prediction and Control of Discrete‐Time First‐Order Linear Stochastic Systems with Prospective Strong Intervention

On Constrained MMVC of Discrete‐Time First‐Order Linear Stochastic Systems with PSI I: The Critically Stable Case

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