![TYPES OF FSS
FSS (Based on shapes by MUNK,[1])– 4 Classes.
1. The center
connected or
N-poles
2. The loop
Types
3. Solid
interiors or
plate types
4.Combinations
of 1,2,3](https://image.slidesharecdn.com/projectann-160803053527/85/Empirical-Study-of-ANN-Based-Prediction-of-Resonant-Frequency-and-Bandwidth-of-Circular-Slot-Loaded-Polygon-FSS-7-320.jpg)
Empirical Study of ANN Based Prediction of Resonant Frequency and Bandwidth of Circular Slot Loaded Polygon FSS
- 1. EMPIRICAL STUDY OF ANN BASED PREDICTION OF RESONANT FREQUENCY AND BANDWIDTH OF CIRCULAR SLOT LOADED POLYGON FSS AUTHOR NAME • M. PANDA • MD RABIUL HOSSAIN • MD EHTESHAM HUSSAIN • SAMRIDDHA SAMANTA • RUBABA RAHMAN Presented by RUBABA RAHMAN
- 2. OUTLINE What is FSS Elements of FSS Design Types of FSS Applications of FSS. Work Done By The Candidate Conclusions
- 3. WHAT IS FREQUENCY SELECTIVE SURFACE ??? A frequency-selective surface (FSS) is any thin, repetitive surface designed to reflect, transmit or absorb electromagnetic fields based on frequency. In this sense, an FSS is a type of optical filter or metal-mesh optical filters in which the filtering is accomplished by virtue of the regular, periodic pattern on the surface of the FSS.
- 4. ELEMENTS OF FSS DESIGN Element Geometries: Patch-type elements-capacitive effect. Aperture-type element-Inductance Effect.
- 5. PATCH-TYPE ELEMENTS The patch-array produces a capacitive response, Band Stop Filter
- 6. APERTURE-TYPE ELEMENT Array of slots is inductive- Band Pass filter
- 7. TYPES OF FSS FSS (Based on shapes by MUNK,[1])– 4 Classes. 1. The center connected or N-poles 2. The loop Types 3. Solid interiors or plate types 4.Combinations of 1,2,3
- 8. APPLICATIONS OF FREQUENCY SELECTIVE SURFACES .. • Frequency selective surfaces have been most commonly used in the radio frequency region of the electromagnetic spectrum • use in applications as diverse as the aforementioned microwave oven, antenna radomes and modern metamaterials. • Sometimes frequency selective surfaces are referred to simply as periodic surfaces and are a 2-dimensional analog of the new periodic volumes known as photonic crystals.
- 9. ARTIFICIAL NEURAL NETWORK (ANN) Artificial Neural Networks (ANNs) are a family of models inspired by biological neural network i.e. the central nervous system of animals, in particular the brain and are used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown. Artificial neural networks are generally presented as systems of interconnected neurons which exchange messages between each other. The connections have numeric weights that can be tuned based on experience, making neural nets adaptive to inputs and capable of learning.
- 10. PROPOSED STRUCTURE OF FSS The geometry of the single layer FSS unit cell structure shown in Fig which consists of polygon with centrally circular slot loaded. The dimension of unit cell structure is clearly mentioned in the said figure. For x, y direction periodicity the length is 11mm & 16mm respectively. The unit cells are placed in repeated structure in 2D Array form 3 mm 10 mm3mm 12mm 11 mm 16mm
- 11. SIMULATION RESULT USING ANSOFT Computer based extensive numerical method is used for designing the parameter of FSS by using different commercial software. Numerical methods are Finite Element Method (FEM), Method of Moment (MOM), Finite Difference Time Domain (FDTD) method etc. For extensive simulation ANSOFT simulation tool is adopted here.
- 12. BAND WIDTH VARIATION FOR DIFFERENT Y DIRECTION PERIODICITY
- 13. RESONANT FREQUENCY FOR DIFFERENT Y DIRECTION PERIODICITY
- 14. ARTIFICIAL ANN FOR PREDICTION OF BW AND RF Back Propagation (BP) is best on MLN perception. Four layer architecture is used here. One input layer, one output layer and two hidden layer is used for the proposed ANN model. For input layer of ANN model the periodicity in x-direction and y-direction are taken. The output of ANN model is the bandwidth and resonating frequency.
- 15. PROPOSE ANN MODEL FOR ESTIMATION OF BAND WIDTH AND RESONANT FREQUENCY
- 16. DATA SEQUENCE FOR ANN Sl no. Periodici ty in x- direction (mm) Periodi city in y- direction (mm) 1 11 18.5 2 11.5 17 3 12.5 17 4 13.5 18 5 14.5 19.5 Sl No. ANSOFT simulati on B.W (GHz) ANN B.W (GHz) 1 4.63 4.75 2 5.49 5.38 3 5.28 5.4 4 4.38 4.28 5 3.53 3.66 Sl No. ANSOFT Simulati on RF (GHz) ANN RF(GHz) 1 10.35 10.43 2 10.17 10.27 3 9.98 10.1 4 9.88 9.76 5 9.62 9.57 INPUT Output for BW Output for RF
- 17. CONCLUSION • All results are quite satisfactory. • Results obtained by proposed ANN structure are compared with ANSOFT simulation tool results. • In both cases result are quite identical and error value is in acceptance range. • For further future study authors intend to take new model with solid or slit loaded structure with different dimensions. • Apart from Neural Network authors concentrate on different types of soft computing tool like PSO,GA, ant colony optimization.