Evaluating each chromosome requires HFSS to analyze the antenna with the parameters suggested by the genes; the results are then used by GA to compute the cost. Pairing, mating and mutation are all included in the new offsprings generation. In our approach, we used the GA to optimize the following parameters in our antenna design: position of the feed probe, length and position of the slits, length and position of the short strip, shape of the polygonal slot, position of shorting pins, and length and width of the patch antenna with minimum changes from its original dimensions listed in Section 1.

During the application of the GA algorithm, twenty chromosomes in each generation are evaluated, cost is computed for each individual as the sum of return losses at desired frequencies Eq. The resultant antenna with optimized parameter is depicted at Fig. The antenna was fabricated and measured at the Microstrip Department of Electronics Research Institute. In the fabrication process, we first applied photolithographic process then we added the short strip and short pin to the antenna. There was difficulty in having precise shorts without air within it.

This caused the final measured results to deviate by 0. A photo of our antenna prototype is shown in Fig. A broadband equivalent circuit model was introduced and a good agreement of input impedance between electromagnetic simulator and modeled equivalent circuit was realized.

This section presents a sample electric circuit that is equivalent to our fabricated antenna. The presented wideband lumped equivalent circuit is necessary when analyzing any electronic circuit that includes our antenna. According to the cavity model Garg et al.

## Design and Analysis of Microstrip Patch Antennas Using Artificial Neural Network

This can be expanded in term of a series of cavity resonant modes; each radiating mode can be represented by a general parallel resonant RLC circuit as shown in Fig. Computing the equivalent circuit parameters over a wideband is done using the optimization technique discussed in Ansarizadeh and Ghorbani, , which uses least square curve fitting to match the equivalent input impedance with the one obtained from the HFSS simulator.

The parameter values are listed in Table 2, and the results from the fitting process are shown in Figs. A novel planar antenna structure was proposed with slots and shorting elements to control surface current distribution on the patch antenna to achieve multi-band operation. We first studied the effect of the reactive loads on the excited modes, and then we applied a genetic search to precisely adjust the antenna parameters.

Experimental measurement to our optimized antenna shows a good matching between the experimental results. AbuTarboush, H.

Ansari, J. H-shaped stacked patch antenna for dual band operation. Progress in Electromagnetic Research B 5, Ansarizadeh, M. An approach to equivalent circuit modeling of rectangular microstrip antennas. Progress in Electromagnetics Research B 8, Cap, A. Multi-band planar antennas: a comparative study. Radioengineering 14 4. PIFA with a meandered and folded patch for the dual-band mobile phone application.

**https://ralimrocano.ga**

## Circularly polarized D‐shaped slot antenna for wireless applications

Constantine, A. Antenna Theory Analysis and Design. Garg, R. Microstrip Antenna Design Handbook. Artech House, Boston, London. Kundukulam, S. Slot-loaded compact microstrip antenna for dual frequency operation. Microwave and Optical Technology Letters 31 5. Novel design method of dualband antenna for WLAN applications. Mishra, A. Compact shorted microstrip patch antenna for dual band operation. Progress in Electromagnetic Research C 9, Paitoon, R.

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Multiband microstrip-fed right angle slot antenna design for wireless communication systems. ETRI Journal 31 3. Pan, B. Low-profile broadband and dual-frequency two-strip planar monopole antennas. Randy, L. Practical Genetic Algorithms. Tawk, Y.

A simple multiband printed bowtie antenna. Dual band omnidirectional planar antenna for WiMAX applications. Wong, K. This algorithm is explained in Section 2. In this algorithm, connections are done from input layer to hidden layer and to output layer. The network is trained using the gradient descent method and the error calculated is sent back to the hidden layer as well as the output layer for weight adjustments [ 13 ]. In multilayer perceptron network there are two sets of weights, namely from input layer to hidden layer and from hidden to output layer.

The error due to the second set of weights is calculated using the delta rule and the error is required to propagate from output layer to input layer so that the error can be assigned proportionately to the weights which caused it. Forward pass : it takes input vector, computes function, and evaluates derivative of error function with respect to weights. Backward pass : it propagates the error derivatives backward and computes the weight adjustments.

The raw values are given to the input layer, computations are done at the hidden layer and their activations are given to the output layer where each pattern is given a specific classification category.

## Microstrip Patch Antennas at GHz for Wireless Power Transfer System to a MAV

In this algorithm, we are giving a solution to credit assignment problem using partial derivatives to find out how weight wij effects the error which is represented by Eq. Here, partial derivatives are used because the direction of the errors can be determined, which may be positive or negative.

If this derivative to be negative, this will add in weight and finally decreases the error and thus local minima can be reached see step 6 of algorithm. This shows that if derivative of error is positive, the weight increases by adding a negative value to the weight and conversely, if negative. It consists of three layers: one input layer, one hidden layer, and one output layer.

Here, x 1, x 2, … , xn are the input neurons. The value of w 1 b is kept constant throughout the process.

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Input, hidden, and output layers can be represented with the help of vectors as illustrated in Eqs. Here a 1 , a 2 , and a P are the desired output vector response. Step 3: Now begin the training process. Input is provided to input layer and output is Calculated as given in Eqs. Step 4: Calculate the new value of error by using previous value of error, desired output a k and actual output O k using Eq. Error signal for output layer is calculated using Eq. Step 6: Now weights of output layer are adjusted using Eq. Step 7: Now weights of Hidden layer are modified using Eq. Step 2 is the initialization step where values of p , k , E , and weights are initialized.

In step 4, errors are computed.

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In step 7, output layer and hidden layer weights are adjusted, respectively. Initial weight : the choice of initial weights determines how fast the networks will converge.

If all the weights are given equal values, the network may not be trained properly. If large values are given to weights at the initial stage, then the system may be stuck at local minima very near to starting point itself. So, it is necessary to initialize with small weights uniformly distributed in a small range. Number of training data : the numbers of input nodes are determined by the dimension, or size or the input vector to be classified or generalized with a certain output quality.

The training data should be sufficient and proper for training. The problem of the oscillations can be sorted out by adding a momentum factor to the weight adjustments as illustrated in Eq. For a network of a reasonable size, the size of a hidden layer has to be only a small fraction of the input layer. For example, if the network does not converge to a solution, it may need more hidden layers. On the other hand, the user may use a small number of hidden layers when the network converges. Stopping criteria : stopping criteria are used to decide when the network problem has been solved.

It is possible to stop when:. IE3D electromagnetic simulation software is used for designing of the rectangular patch antenna using the following design parameters. Now, the dimensions of patch can be calculated using Eqs. Here, 50 ohm port Z o is connected to the patch by using the microstrip feed line technique. Feed is the point where patch radiates maximum. When IE3D electromagnetic simulation is performed, then we obtain the different resultant graph between return loss and frequency.

Here all the dimensions are taken in mm. Here, 4.