Journal of The Institution of Engineers, Khor, and M. International Journal of Approximate Reasoning, Control Engineering Practice, Chen, and D. Ambekar1 and Madhuri A. Chaudhari2, 1K. College of Engg. However, generated harmonics cause a nuisance in power system operation.
The work presented here, deals with reduction of harmonics 3rdth by using Multiple Pulse Modulation technique. Traditional numerical methods do not yield accurate pulse-positions as non-linearity is involved in computation. Results show considerable improvement in voltage spectrum if trigger pulses are generated at the ANN positions as harmonic contents are reduced with significant improvement in fundamental voltage resulting in reduction in device ratings.
The quantitative analysis is given in tabular form. This shows feasibility of design of a controller for optimized performance of a single phase VSI.
Jang, G. Kirlin, Sam Kwok, S. Legowski, A. R Karshenas, H. Kajori, S. Krishnamoorthy, G. Dubey, G. IEE, vol. Bowes, P. Ambekar, A. Mehrotra, C. Mohan, S. Ranka, Penram International Publishing, India. Joy Mazumdar, R. Harley, F. Lambart, G. On Power Electronics, vol. Joseph Jawhar, N. John W. On Industrial Electronics, vol. Aldair1 and Weiji J. However, the current active suspension system has a high energy consumption therefore reducing the fuel economy.
In this paper the vibration excited by road unevenness is treated as a source of mechanical energy. It is being converted into electrical energy to compensate for the energy consumption by the active suspension. To achieve this task, an electromagnetic active suspension system has been introduced.
The power generated from this device has been used as input power of the pump of the hydraulic actuators. Adaptive neuro-fuzzy controllers have been designed to generate a signal to control the valves of the hydraulic actuators. Ren, and Senlin, L. Shian, and L. ZL Transactions of the Chinese Society for Agricultural Machinery, Journal of Shanghai Jiaotong University, Motor Technology, Vehicle System Dynamics, Guo, and J.
Advanced Materiral Research, Smart Mater. Structure, Fan, and Z. Shanghai Tiaotong University, Engineering Practice, Ibrahim and Mohamed A. The results of neural networks are obtained but its result is not in comprehensible form or in a black box form. Our goal is to use an important and desirable model to identify sets of input variable which results in a desired output value.
The nature of this model can help to find an optimal set of difficult input variables. Genetic algorithms are used as an interpretation of achieving neural network inversion. On the other hand the inversion of neural network enables to find one or more input patterns which satisfy a specific output. The input patterns obtained from the genetic algorithm can be used for building neural network system explanation facilities.
Giles, D. Chen, G. Sun, H. Chen, Y. Lee, M. Goudreau, Constructive learning of recurrent neural networks: Limitations of recurrent cascade correlation and a simple solution, IEEE Trans. Neural Networks 6 1 Parekh, J. Yang, V. Littmann, H. Ritter, Learning and generalization in cascade network architectures, Neural Computation 8 7 Prechelt, Investigation of the casCor family of learning algorithms, Neural Netwrks 10 5 Smieja, Neural network constructive algorithms: Trading generalization for learning efficiency, Circuits, Systems and Signal Processing 12 2 Wotawa, G.
Wotawa, Deriving qualitative rules from neural networks-a case study for Ozone forecasting,AI Commun. Narayanan, E. Keedwell, D. Goebel, L. Han and M. Towell and J. Shavlik, The extraction of refined rules from knowledge-based neural networks, Machine Learning 13 1 Andrews, J. Diederich, A. Tickle, Survey and critique of techniques for extracting rules from trained artificial neural networks, Knowledge Based System 8 6 Krishnan, G.
Sivakumar, P. Bhattacharya, A search technique for rule extraction from trained neural networks, Pattern Recognition Let- ters 20 3 Neural Networks 11 2 Setiono, Extracting rules from neural networks by pruning and hidden-unit splitting, Neural Computation 9 1 Dissertation, Univ. Wisconsin, Jiang , S. Chen, Extracting symbolic rules from trained neural network ensembles, AI Commun. Markowska-Kaczmar and M. Chumieja, Discovering the mysteries of neural networks, International Journal of Hybrid Intelligent Systems 1 3,4 Santos, j.
Nievola, A. Freitas, Extracting comprehensible rules from neural network via genetic algorithms, in: Proc. An artificial neural network is a system and this system is a structure which receives an input, processes the data and provides an output. The input in data array will be WAVE sound, a data from an image file or any kind of data that can be represented in an array.
Once an input is presented to the neural network required target response is set at the output and from the difference of the desired response along with the output of real system an error is obtained.
The error information is fed back to the system and it makes many adjustments to their parameters in a systematic order which is commonly known as the learning rule.
This process is repeated until the desired output is accepted. It is observed that the performance hinges heavily on the data, so the data should be pre-processed with third party algorithms such as DSP algorithms. There are different types of Artificial Neural Networks ANN — Depending upon the human brain neuron and network functions, an artificial neural network or ANN performs tasks in a similar manner. Most of the artificial neural networks will have some resemblance with more complex biological counterparts and are very effective at their intended tasks like for e.
Types of Artificial Neural Networks. The feedback network feeds information back into itself and is well suited to solve optimization problems, according to the University of Massachusetts, Lowell Center for Atmospheric Research.
Feedback ANNs are used by the Internal system error corrections. Feed Forward ANN — A feed-forward network is a simple neural network consisting of an input layer, an output layer and one or more layers of neurons. Through evaluation of its output by reviewing its input, the power of the network can be noticed base on group behavior of the connected neurons and the output is decided. The main advantage of this network is that it learns to evaluate and recognize input patterns.
An artificial neural network is a computational simulation of a biological neural network. These possess the behavior of neurons and the electrical signals in which they communicate between input such as from the eyes or nerve endings in the hand to the output of the brain such as reacting to light, touch or heat.
Scientists were researching in the designing of artificial neural networks and the creation of artificial intelligence about the way neurons semantically communicate. Neural network simulators are software applications that are used to simulate the behavior of artificial or biological neural networks. They focus on one or a limited number of specific types of neural networks.
Propagation is uni-directional where CNN contains one or more convolutional layers followed by pooling and bidirectional where the output of convolution layer goes to a fully connected neural network for classifying the images as shown in the above diagram. Filters are used to extract certain parts of the image. In MLP the inputs are multiplied with weights and fed to the activation function.
Convolution neural networks show very effective results in image and video recognition, semantic parsing and paraphrase detection. Radial Basis Function Network consists of an input vector followed by a layer of RBF neurons and an output layer with one node per category.
This will be one of the examples from the training set. When a new input vector [the n-dimensional vector that you are trying to classify] needs to be classified, each neuron calculates the Euclidean distance between the input and its prototype. For example, if we have two classes i. Hence, it could be tagged or classified as class A. Each RBF neuron compares the input vector to its prototype and outputs a value ranging which is a measure of similarity from 0 to 1.
As the input equals to the prototype, the output of that RBF neuron will be 1 and with the distance grows between the input and prototype the response falls off exponentially towards 0. The output layer consists of a set of neurons [one per category]. Application: Power Restoration a. Powercut P1 needs to be restored first b. Powercut P3 needs to be restored next, as it impacts more houses c. Powercut P2 should be fixed last as it impacts only one house.
There are three types of gates viz, Input gate, output gate and forget gate. Input gate decides how many information from the last sample will be kept in memory; the output gate regulates the amount of data passed to the next layer, and forget gates control the tearing rate of memory stored.
This architecture lets them learn longer-term dependencies. A sequence to sequence model consists of two Recurrent Neural Networks. Here, there exists an encoder that processes the input and a decoder that processes the output. The encoder and decoder work simultaneously — either using the same parameter or different ones. This model, on contrary to the actual RNN, is particularly applicable in those cases where the length of the input data is equal to the length of the output data.
While they possess similar benefits and limitations of the RNN, these models are usually applied mainly in chatbots , machine translations, and question answering systems. A modular neural network has a number of different networks that function independently and perform sub-tasks. The different networks do not really interact with or signal each other during the computation process.
They work independently towards achieving the output. As a result, a large and complex computational process are done significantly faster by breaking it down into independent components. The computation speed increases because the networks are not interacting with or even connected to each other.
Neural Networks are artificial networks used in Machine Learning that work in a similar fashion to the human nervous system. Many things are connected in various ways for a neural network to mimic and work like the human brain. Neural networks are basically used in computational models. A deep neural network DNN is an artificial neural network ANN with multiple layers between the input and output layers. They can model complex non-linear relationships.
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