Neural Networks
A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature.
Neural networks can adapt to changing input; so the network generates the best possible result without needing to redesign the output criteria. The concept of neural networks, which has its roots in artificial intelligence, is swiftly gaining popularity in the development of trading system.
Types of Neural Networks
#Feed-Forward Neural Networks
Feed-forward neural networks are one of the more simple types of neural networks. It conveys information in one direction through input nodes; this information continues to be processed in this single direction until it reaches the output mode. Feed-forward neural networks may have hidden layers for functionality, and this type of most often used for facial recognition technologies.
#Recurrent Neural Networks
A more complex type of neural network, recurrent neural networks take the output of a processing node and transmit the information back into the network. This results in theoretical “learning” and improvement of the network. Each node stores historical processes, and these historical processes are reused in the future during processing. This becomes especially critical for networks in which the prediction is incorrect; the system will attempt to learn why the correct outcome occurred and adjust accordingly. This type of neural network is often used in text-to-speech applications.
#Convolutional Neural Networks
Convolutional neural networks, also called Conv Nets or CNNs, have several layers in which data is sorted into categories. These networks have an input layer, an output layer, and a hidden multitude of convolutional layers in between. The layers create feature maps that record areas of an image that are broken down further until they generate valuable outputs. These layers can be pooled or entirely connected, and these networks are especially beneficial for image recognition applications.
Deconvolutional Neural Networks
Deconvolutional neural networks simply work in reverse of convolutional neural networks. The application of the network is to detect items that might have been recognized as important under a convolutional neural network. These items would likely have been discarded during the convolutional neural network execution process. This type of neural network is also widely used for image analysis or processing.
Modular Neural Networks
Modular neural networks contain several networks that work independently from one another. These networks do not interact with each other during an analysis process. Instead, these processes are done to allow complex, elaborate computing processes to be done more efficiently. Similar to other modular industries such as modular real state, the goal of the network independence is to have each module responsible for a particular part of an overall bigger picture.
Multi-Layered Perceptron
In a multi-layered perceptron (MLP), perceptron are arranged in interconnected layers. The input layer collects input patterns. The output layer has classifications or output signals to which input patterns may map. For instance, the patterns may comprise a list of quantities for technical indicators about a security; potential outputs could be “buy,” “hold” or “sell.”
Hidden layers fine-tune the input weightings until the neural network’s margin of error is minimal. It is hypothesized that hidden layers extrapolate salient features in the input data that have predictive power regarding the outputs. This describes feature extraction, which accomplishes a utility similar to statistical techniques such as principal component analysis.
Applications
Neural networks can be used in different fields. The tasks to which artificial neural networks are applied tend to fall within the following broad categories:
- Function approximation, or regression analysis, including time series prediction and modeling.
- Classification, including pattern and sequence recognition, novelty detection and sequential decision making.
- Data processing, including filtering, clustering ,blind signal separation and compression.
Application areas of ANNs include non linear system identification and control (vehicle control, process control), game-playing and decision making (backgammon, chess, racing), pattern recognition (radar systems, face identification, object recognition), sequence recognition (gesture, speech, handwritten text recognition), medical diagnosis, financial applications, data mining(or knowledge discovery in databases, “KDD”), visualization and e-mail span filtering. For example, it is possible to create a semantic profile of user’s interests emerging from pictures trained for object recognition.
