**7. References**

60 Fuzzy Inference System – Theory and Applications

station where there is the risk of explosion, Fig. 13(b). The simulation results show the fact that the introduced algorithm is flexible in terms of the environmental conditions and the

To further illustrate the conceptual basis of the utilized potential field, a 3D representation of the risk potential function and the corresponding optimal path are represented in Fig. 14.

Fig. 14. Artificial potential field and the obtained path with minimum risk

A new fuzzy approach is introduced to perform a more applicable risk analysis in real world applications. This procedure is used to determine the multi-purpose criticalities of activities where six main factors V, SFA, SCA, PFA, RLA and COR are considered as criticality indexes. A fuzzy inference system with three inputs: probability of impact, impact treat, and ability to retaliate is used to calculate the values of RLA for activities. The output of FIS represents the risky level of each activity. The decision values obtained by classic multi criteria decision making problem are then considered as criticality indexes of activities. The obtained results are compared to classic PERT, from the view point of impact expenses, by using the Mont Carlo method. It has been shown that by considering the multipurpose criticalities (instead of total slacks) a considerable amount of expenses caused by different impacts may be saved. The introduced method is applied to simultaneous task scheduling and path planning of rescue robots. Simulation results show that project management technique along with risk analysis by means of artificial potential field path planning is an efficient tool which may be used for rescue mission scheduling by intelligent robots. The algorithm is flexible in terms of environmental situation and the effective factors in risk analysis. In fact the proposed method merges the path planning methods with rescue

factors involved in targets.

**6. Conclusion** 

mission scheduling.

Alan, A., & Pritsker, B. (1966). GERT: *Graphical Evaluation and Review Technique*, Rand Corp


**4** 

*Malaysia* 

**A Concise Fuzzy Rule Base to Reason Student** 

**Performance Based on Rough-Fuzzy Approach** 

A fuzzy inference system employing fuzzy if then rules able to model the qualitative aspects of human expertise and reasoning processes without employing precise quantitative analyses. This is due to the fact that the problem in acquiring knowledge from human experts is that much of the information is uncertain, inconsistent, vague and incomplete (Khoo and Zhai, 2001; Tsaganou et al., 2002; San Pedro and Burstein, 2003; Yang et al., 2005). The drawbacks of FIS are that a lot of trial and error effort need to be taken into account in order to define the best fitted membership functions (Taylan and Karagözoğlu, 2009) and no standard methods

Evaluation and reasoning of student's learning achievement is the process of determining the performance levels of individual students in relation to educational objectives (Saleh and Kim, 2009). Although Fuzzy inference system is a potential technique to reason the student's performance, as well as to present his/her knowledge status (Nedic et al., 2002; Xu et al., 2002; Kosba et al. 2003), it is a challenge when more than one factor involve in determining the student's performance or knowledge status (Yusof et. al, 2009). Hence, the reasoning of the student's performance for multiple factors is difficult. This issue is critical considering that the human experts' knowledge is insufficient to analyze all possible conditions as the

Addressing these matters, this work proposes a Neuro-Fuzzy Inference System (ANFIS), which combines fuzzy inference system and neural network, in order to produce a complete fuzzy rule base system (Jang, 1993). The fuzzy system represents knowledge in an interpretable manner, while the neural networks have the learning ability platform to optimize its parameters. Hence, ANFIS has the capability to perform parameter-learning rather than structural learning (Lin and Lu, 1996). ANFIS is expected to recognize other decisions that are previously not complete, in both the antecedents and consequent parts of the fuzzy rules. Unfortunately, too many fuzzy rules will result in a large computation time and space (Jamshidi, 2001). Therefore, reduction of knowledge is possible to be applied to determine the selection of important attributes that can be used to represent the decision system (Chen, 1999) based on the theory of rough sets. Fig. 1 shows the proposed fuzzy

exist for transforming human knowledge or experience into the rule base (Jang, 1993).

information gained is always incomplete, inconsistent, and vague.

**1. Introduction** 

inference system.

Norazah Yusof, Nor Bahiah Ahmad,

 *Universiti Teknologi Malaysia, Skudai, Johor* 

Mohd. Shahizan Othman and Yeap Chun Nyen *Faculty of Computer Science and Information System,* 

