Artificial Neural Networks (ANNs) are currently a hot topic of research, attracting researchers from a wide range of fields. Biology, computing, electronics, mathematics, medicine, physics, and psychology all contribute to this study.
The methods to this issue, as well as the goals, are quite different. The main concept is to build intelligent artificial systems using an understanding of the nervous system and the human brain.
(Read More: Introduction to Neural Network and Deep Learning )
On the one hand, biologists and psychologists are attempting to model and comprehend the brain and components of the nervous system, as well as to find explanations for human behavior and reasons for the brain's limits.
Computer scientists and electronic engineers, on the other hand, are looking for more efficient solutions to address issues that are now solved with traditional computers. These scientists frequently draw inspiration from physiological and behavioral models and concepts.
Different types of ANN
Watch this video on Neural Networks and it’s working:
A modular neural network is made up of several neural network models that are linked together via an intermediate. Modular neural networks allow for more complex management and handling of more basic neural network systems.
In this case, the multiple neural networks act as modules, each solving a portion of the issue. An integrator is responsible for dividing the problem into multiple modules as well as integrating the answers of the modules to create the system's final output.
Modular neural networks have been studied in various methods since the 1980s. A collection of "simple" or "weak" learners can outperform a single deep learning model, according to the idea of ensemble learning.
(Also Read: Applications of Neural Networks)
The "divide and conquer" principle, which divides large problems into more manageable parts, and diversity promotion, which some experts define as a biologically-based model in which different types of neural networks cooperate, each performing a different role or function, are both important principles in modular neural network development.
Some of the benefits of Modular Neural Network include Efficiency, Robustness and Independent training while one considerable disadvantage is the issues encountered in moving with the target.
Structure of Modular Neural Network (source)
Experts may also use the terms "tightly linked modular neural network models" and "loosely coupled modular neural network models" to describe the connection between the network components.
Modular neural networks, in general, allow engineers to expand the possibilities of employing these technologies to push the limits of what neural networks can do.
Each network is converted into a module that may be freely combined with modules of different sorts. We get to the notion of modular neural networks in this way.
(Also Read: How is Transfer Learning done in Neural Networks)
Reducing model complexity: Controlling the degrees of freedom of the system is one method to minimize training time.
Data fusion and prediction averaging: Network committees may be thought of as composite systems consisting of comparable parts.
Combination of techniques: As a building block, more than one method or network class can be utilized.
Learning several tasks at the same time: Trained modules can be transferred between systems that are built for various tasks.
Robustness and incrementality: The integrated network may be fault-tolerant and develop progressively.
As integrated architectures, modular neural networks have a biological background: Natural brain systems are made up of a hierarchy of networks made up of components that are each specialized for a certain job. In general, integrated networks outperform flat, unstructured networks.
(Related: Common Architectures in Convolution Neural Network )
The modules are partially self-contained, allowing the system to run in parallel. It is always required to have a control system for this modular approach in order for the modules to function together in a meaningful manner.
Stock market forecasting software
Character recognition with adaptive MNN
High-level input data compression
The Backpropagation technique is used to train in two phases. All sub-networks in the input layer are trained in the first phase. The components of the original vector that are connected to this specific network (as an input vector) are combined with the intended output class expressed in binary or l-out-of-k coding in the individual training set for each sub-network, which is picked from the original training set.
Read this thesis submitted to the Manchester Metropolitan University on Modular neural Networks by Albert Schmidt.
For two reasons, training a new network design on the same issue is faster than training a monolithic modular network on the same problem:
(1) In a modular network, the number of connections and hence the number of weights is significantly lower than in a monolithic MP. During BP training, fewer weights mean fewer operations. This has the immediate effect of speeding up the learning process.
(2) Because the modules in the input layer are self-contained, the training may be done in parallel. The maximum time required to train one of the input modules plus the time required to train the decision module equals the total training time for a complete parallel implementation.
As a result, in parallel training, the number of weights that must be considered as a time factor is limited to the number of weights in an input module plus the number of weights in the decision module.
The division of the training set into subgroups might potentially cause issues. Especially for modules with a limited number of input variables, the number of identical input vectors with distinct potential output values may rise.
Refer to this Study on “Modular neural network architecture”
The use of modular and only partially linked neural networks is advocated, which is inspired by nature. This model combines two distinct generalization techniques, resulting in improved generalization performance.
The use of a modular design is proven in experiments. This method of expanding networks appears to be highly promising. The use of a modular design makes training easier and faster for many real-world data sets. Parallel training is simple due to the independence of the modules in the input layer.
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