Showing posts with label ART 1. Show all posts
Showing posts with label ART 1. Show all posts

AI Glossary - What Is The ART 1 Algorithm?


    What Is ART 1?

    The initial Adaptive Resonance Theory (ART) model was designated as ART 1. 

    It has the ability to cluster binary input variables.

    What Is The Architecture And Design Of ART 1?

    The Design of ART1

    The following two units make up ART1:

    Parameters Used Above:

    n − Number of components in the input vector

    m − Maximum number of clusters that can be formed

    bij − Weight from F1b to F2 layer, i.e. bottom-up weights

    tji − Weight from F2 to F1b layer, i.e. top-down weights

    ρ − Vigilance parameter

    ||x|| − Norm of vector x

    1. Computational Unit Of ART 1

    It consists of the following:

    (i) Unit of input (F1 layer) 

    It also includes the next two parts:

    1. F1 a layer Input portion – In ART1, this part would merely include the input vectors with no processing. It has an F1b layer interface portion connection.

    2. F1b layer interface portion - The signal from the input section and the signal from the F2 layer are combined at the F1b layer interface portion. Bottom up weights bij connect the F1b layer to the F2 layer, while top down weights tji link the F2 layer to the F1b layer.

    2. Cluster Unit (F2 layer): 

    This is a layer that is in competition. To learn the input pattern, the unit with the highest net input is chosen. All other cluster units have their activation set to 0.

    3. Reset Method: 

    This mechanism operates by comparing the input vector's similarity to the top-down weight. The cluster will not be permitted to learn the pattern if the degree of similarity is less than the vigilance parameter, and a rest will take place.

    4. Supplement Unit: 

    In reality, the problem with the reset mechanism is that the layer F2 has to be suppressed under certain circumstances and also needs to be accessible while learning occurs. Because of this, the supplementary units G1 and G2 as well as the reset unit R were introduced. Gain control units are what they are known as. These units communicate with the other units in the network by receiving and sending signals. An inhibitory signal is denoted by a "," whereas an excitatory signal is denoted by a "+."

    What Is The Adaptive Resonance Theory ART 1 Algorithm?

    Step 1 − Initialize the learning rate, the vigilance parameter, and the weights as follows −



    Step 2 − Continue step 3-9, when the stopping condition is not true.

    Step 3 − Continue step 4-6 for every training input.

    Step 4 − Set activations of all F1a and F1 units as follows

    F2 = 0 and F1a = input vectors

    Step 5 − Input signal from F1a to F1b layer must be sent like


    Step 6 − For every inhibited F2 node

    yj=∑ibijxi the condition is yj ≠ -1

    Step 7 − Perform step 8-10, when the reset is true.

    Step 8 − Find J for yJ ≥ yj for all nodes j

    Step 9 − Again calculate the activation on F1b as follows


    Step 10 − Now, after calculating the norm of vector x and vector s, we need to check the reset condition as follows −

    If ||x||/ ||s|| < vigilance parameter ρ,⁡then⁡inhibit ⁡node J and go to step 7

    Else If ||x||/ ||s|| ≥ vigilance parameter ρ, then proceed further.

    Step 11 − Weight updating for node J can be done as follows −



    Step 12 − The stopping condition for algorithm must be checked and it may be as follows −

    Do not have any change in weight.

    Reset is not performed for units.

    Maximum number of epochs reached.


    Frequently Asked Questions:

    What distinguishes ARTs 1 and 2 from one another?

    The ART1 architecture is the most basic and straightforward. 

    It can cluster input values with binary data. 

    ART2 is an enhancement of ART1 that can cluster input data with continuous values.

    What is the Process of Adaptive Resonance Theory?

    A cognitive and neurological theory called adaptive resonance theory, or ART, explains how the brain develops its own ability to attend to, classify, identify, and anticipate items and events in a dynamic environment. 

    The current most comprehensive set of cognitive and neurological theories for explanation and prediction is ART.

    What Is The ART Network?

    The ART network is essentially a vector classifier that receives an input vector and categorizes it into one of the categories based on which stored pattern it most closely matches.

    What Is Fuzzy ART?

    Fuzzy ART uses fuzzy set theory calculations to train the ART 1 neural network to classify solely binary input patterns.

    Reference And Further Reading

    • Tayyebi, S. and Soltanali, S., Fuzzy Modeling System Based on Ga Fuzzy Rule Extraction and Hybrid of Differential Evolution and Tabu Search Approaches: Application in Synthesis Gas Conversion to Valuable Hydrocarbons Process. Saeed, Fuzzy Modeling System Based on Ga Fuzzy Rule Extraction and Hybrid of Differential Evolution and Tabu Search Approaches: Application in Synthesis Gas Conversion to Valuable Hydrocarbons Process.
    • Tang, Y., Qiu, J. and Gao, M., 2022. Fuzzy Medical Computer Vision Image Restoration and Visual Application. Computational and Mathematical Methods in Medicine2022.
    • Dymora, P., Mazurek, M. and Bomba, S., 2022. A Comparative Analysis of Selected Predictive Algorithms in Control of Machine Processes. Energies 2022, 15, 1895.
    • Naosekpam, V. and Sahu, N., 2022, April. IFVSNet: Intermediate Features Fusion based CNN for Video Subtitles Identification. In 2022 IEEE 7th International conference for Convergence in Technology (I2CT) (pp. 1-6). IEEE.
    • Boga, J. and Kumar, V.D., 2022. Human activity recognition by wireless body area networks through multi‐objective feature selection with deep learning. Expert Systems, p.e12988.
    • Župerl, U., Stepien, K., Munđar, G. and Kovačič, M., 2022. A Cloud-Based System for the Optical Monitoring of Tool Conditions during Milling through the Detection of Chip Surface Size and Identification of Cutting Force Trends. Processes10(4), p.671.
    • Neto, J.B.C., Ferrari, C., Marana, A.N., Berretti, S. and Bimbo, A.D., 2022. Learning Streamed Attention Network from Descriptor Images for Cross-resolution 3D Face Recognition. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM).
    • Chattopadhyay, S., Dey, A., Singh, P.K., Ahmadian, A. and Sarkar, R., 2022. A feature selection model for speech emotion recognition using clustering-based population generation with hybrid of equilibrium optimizer and atom search optimization algorithm. Multimedia Tools and Applications, pp.1-34.
    • Kanagaraj, R., Elakiya, E., Rajkumar, N., Srinivasan, K. and Sriram, S., 2022, January. Fuzzy Neural Network Classification Model for Multi Labeled Electricity Consumption Data Set. In 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 1037-1041). IEEE.

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