TACDSS: Adaptation Using a Hybrid Neuro-Fuzzy System

1. TACDSS: Adaptation Using a Hybrid Neuro-Fuzzy System Cong Tran & Lakhmi Jain School of Electrical and Information Engineering University of South Australia, Australia Ajith Abraham Faculty of Information Technology, School of Business Systems, Monash University

2. Propose of the project KES • To Investigate AI techniques to build a Tactical Air Combat Decision Support System (TACDSS). • Creating the system that is able to learn data or information and extract the knowledge in the form of If-Then rules as requirement by the user. • The knowledge rules will be evaluated by computer simulation or by human experts. If the developed rule base is not satisfactory then the optimisation process will be applied to provide a better solution.

3. The model of database learning for decision support system KES

4. Hybrid Neuro-Fuzzy Model KES • The advantage of Neural Network is a system will be trained from data. The system can recognise the object that is known (supervised) or unknown (unsupervised). The disadvantage of Neural network is that they cannot implement the human knowledge and in some way, people think the operation of neural network are like a black box. • The advantage of the fuzzy logic technique is that it can implement the human knowledge using the If-Then rule. The disadvantage of fuzzy logic is that they are not adaptive. • Combination of both techniques will create a better system that can be adaptive and automatically implement a knowledge base for decision support systems.

5. Case study: Tactical Air Combat Environment (TACE) KES • The combat air patrol (CAP) has two friendly hornets (F/A-18s) and other two hostiles at the ground base (as plus sign), an air-to-air fuel tanker (KB707) (as square sign) and four hostiles aircraft Mig-29 as (circle sign). • The mission operator has few options to make a decision on the allocation of hornets to intercept the enemy aircraft. - Send the Hornet directly to the spotted area and intercept. - Call the Hornet in the area back to ground base and send another hornet from the ground base. - Call the Hornet in the area for refuel before intercepting theenemy aircraft.

6. Case study: Tactical Air Combat Environment (TACE) KES

7. Decision factors of the TACE KES

8. The knowledge of TACE KES There are two important decision selection rules, which wereformulated using expert knowledge: • 1. The decision selection will have small value if the fuel used being too low, the interrupt time is too long, the hornet has low weapon status and the situation awarenessof friend or enemy (FOE) is high value. • 2. The decision selection will have high value if the fuel used being full, the interrupt time is fast enough, the hornet has high weapon status and the situation awareness of of friend or enemy(FOE) is low value.

9. Adaptive Network based Fuzzy Inference System- ANFIS KES • The ANFIS is the hybrid neural-fuzzy system, which was developed by Jang. The system is based on the architecture of the Takagi-Sugeno fuzzy inference system. The fuzzy rule is set up as If x is Aiand y is Bi then fi = pix + qiy + ri where i is an index i = 1,2,3. p, q and r is parameter set of function f • The ANFIS has six-layeredstructure which the function of each layer is known as - Layer 1 is Input layer, the output of this node is the input values of the ANFIS - Layer 2 is Fuzzification layer, the node in this layer is the membership functions of input linguistic variable • - Layer 3 is Antecedent layer, The output of nodes in this layer is the product of all the incoming signals which denotes O3,n = Wn= mAi(x) x mBi(y), where i = 1,2 and 3, n is number of fuzzy rule

10. Adaptive Network based Fuzzy Inference System- ANFIS KES • Layer 4 is Rule strength layer, The node in this layer is an adaptive node with the node function calculates the ratio of the ith fuzzy rules firing strength (RFS) to the sum of RFS. O4,n= = where n = 1,2,..,81 • Layer 5 is Consequence layer, the node in this layer is an adaptive node being defined as O5,n = = (pnx + qny + rn) where pn, qn and rnare the parameter set of the particular node and is referred to as consequent parameters. • Layer 6 is Output layer, this layer provides the defuzzification process using the technique of centre of gravity to computes the overall output.

11. Architecture of ANFIS KES

12. Hybrid learning of ANFIS KES • A step in the learning procedure has two parts: In the first part the input patterns are propagated, and the optimal conclusion parameters are estimated by an iterative least mean square procedure, while the antecedent parameters (membership functions) are assumed to be fixed for the current cycle through the training set. • In the second part the patterns are propagated again, and in this epoch, back propagation is used to modify the antecedent parameters, while the conclusion parameters remain fixed.

13. Experimental result of TACDSS KES • Comparison of MF before and after training using the hybrid learning method

14. Testing of the developed TACEDSS KES • This part tests the knowledge of the TACDSS with changing the input values and checking the result following the TACE rules - When the “fuel used” was set at 0.2, the solution obtained was 0.0922 and when the fuel tank was set at 0.9 then the solution was 0.965. - When the “interrupt time” was set to 0.2, the solution was 0.421 and when the setting was increased to 0.9, the solution obtained was 0.399. - For “weapon efficiency” set at 0.1, the decision score obtained was 0.434 and the decision core increased to 0.524 when the setting was increased to 0.9. - When the “danger situation” is set at 0.2, the solution obtained was 0.471 and for danger situation set at 0.9, the score was reduced to 0.154. • The results demonstrate that the developed TACE fuzzy inference system could provide the decision scores as same as a tactical air combat expert.

15. Comparison the shape of membership function KES • We used different shapes of MFs for input and output variables to obtain the root means square error (RMSE) after 15 epochs.

16. Effect of increasing number of membership functions KES • We increased number of membership function of input and output linguistic variables from 3 to 4 MFs and compared the solution of the developed TACEDSS in each case. • With a setting of fuel used 0.9, interrupt time 0.1, weapon efficiency 0.9 and danger situation 0.1, the decision score for 3 MFs was 0.865 being greater than for 4 MFs = 0.857. Hence we may conclude that with more MFs per ILV one could improve the accuracy of the decision scores

17. Testing the TACDSS KES • We have extracted 20 percent of master data set to form the test data of TACDSS. The remaining master data set was selected as the training data. • Only input data of the test data set is passed to the TACDSS and the comparison between an actual output and an expected output data is compared. (RMSE on test data = 1.4078 e-5)

18. Comparison between actual and expected output of TACDSS using 20 % test data KES

19. Conclusion KES • In this paper, we have proposed the automatic construction of TACDSS using a Takagi-Sugeno neuro-fuzzy system. We used ANFIS algorithm for the automatic construction of if-thenfuzzy rules using neural network learning techniques. • The simulation result demonstrates the importance of the shape and number of membership functions for each input variablefor obtaining the best performance. • Our future research will be oriented to develop other soft computing techniques such as Mamdani neuro fuzzy systems, decision tree analysis and unsupervised learning methods and compare theresults with current and previous works.