Essay On Feed Forward Back Propagation

The objective of the neural network is to transform the input to meaningful output. Neural networks are often used for statistical analysis and data modeling. Neural network has many uses in data processing, robotics, and medical diagnosis [2]. From the starting of the neural network there are various types found, but each and every types has some advantages and disadvantages. Deep learning and -neural network software are the categories of artificial neural network. The parallel process also allows ANNs to process the large amount of data very efficiently. The artificial neural network is built with a systematic

2. Radius; increasing the radius will increase the inductance value and the Q of the device; somehow it will resonate at high frequency.

The following steps are used to design the back propagation neural network algorithm for the proposed research work. The first step is to set the input, output data sets. The second step is to set the number of hidden layer and output activation functions. The third step is to set the training functions and training parameters, finally run the network.

We get that the percent error for method 1 is 19.3% and the percent error for method 2 is 1.2%. This affirms that the method 2 is the more accurate method to measure the resonant frequency. This may be due to the fact that method 1 involves using estimations by eye, which allows for human error and inaccurate estimations. The difference between the experimental and theoretical values for the resonant frequency could also result from faulty equipment, which may not function as the manufacturer claims or be worn down by natural decay.

Here first we design a simple rectangular shaped microstrip patch antenna with dimension 14 X 14 X 1.60 mm^3 shown in Fig.1. In this antenna the dimension of patch is 10 X 13 mm^2. We are using microstrip line feed to this rectangular patch with length 4mm and width 1.6mm. By simulating this simple patch antenna we were getting only two frequency bands as shown in Fig2.

The processing unit of a single - layer perceptor network can be able to solve the

Among numerous choices, it is the most simple and result oriented approach. After selecting the resonant mode, it is the resonator’s dielectric constant value and its aspect ratio (a/h) that define the obtainable bandwidth. In simple geometric shapes such as cylindrical DRAs the smallest possible εr value makes Q-factor low which as a consequence results in wider bandwidth [34].

For this week’s lab we are using various scenarios to compare and contrast different antenna types and solutions to reduce signal spillover in a specific wireless network. Also we are evaluating and

The overall learning algorithm now proceed as follows; first, propagate the input forward using equation 3.3 and equation 3.4; next, propagate the sensitivities back using equation 3.15 and equation 3.12; and lastly, update the weights and offset using equation 3.7, equation 3.8, equation 3.10 and equation 3.11. (Murphy,

The training is divided into two phases: learning phase and testing phase. In the learning phase, an iterative which updated the synoptic weights is formed upon the error BP (Back Propagation) algorithm. In the testing phase the number of input and output parameters as well as the cases number influenced the neural network,whereas the trained results is then compared to the target to make a decision about the continuing of the iteration or the obtained results is concluded. The common ANN structure for the three architectures is (3X3), which means three neurons in the input layer and three neurons in the hidden layer. The training of each ANN architecture designs are shown in the following: fig.3, fig.4 and fig.5,

Where Zo is the CPE constant, W is the angular frequency (in rad/s), i2 = -1 is the imaginary number and (n) is the CPE exponent. Based on (n), CPE can represent resistance (n = 0, Zo = R),

Structure analysis is very important in design of MEMS devices in order to optimize the device performance. In the following step of this paper an analysis of a doubled ended tuning fork in the presence of an axial forced is performed. Then scale factor for this sensor is derived from relationship between natural frequency of the resonator and the applied

The equation for radiation resistance is given in (1). As can be seen, the greater the radiation resistance, the more energy is radiated or received by the antenna. When the radiation resistance of the antenna matches the resistance of the transmitter or receiver, the system is optimized. Antennas also have ohmic or loss resistance which decreases efficiency. It can be shown that an efficient antenna must be comparable to a wavelength in size.

Microstrip antenna has quickly evolved from academic novelty to commercial reality with applications in a wide variety of microwave systems. In fact rapidly developing markets in a personal communications systems

Fundamental characteristics of this antenna such as the radiation directivity and the frequency response are measured. Figure shows a picture of the proposed antenna that has a shape of ―tears drop‖. Using Novel Ultra Wideband Planar Reflector Antenna a reflector antenna relies on reflection of energy from the reflector. Reflector antennas tend to be good directional and high gain antennas [11]. There are some classes of ultra wideband reflector antennas, such as planar, corner, and parabolic cylinder reflector antennas.