Showing posts with label Adaptive Resonance Theory. Show all posts
Showing posts with label Adaptive Resonance Theory. 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.

    AI Glossary - What Is ARTMAP?


      What Is ARTMAP AI Algorithm?

      The supervised learning variant of the ART-1 model is ARTMAP.

      It learns binary input patterns that are given to it.

      The suffix "MAP" is used in the names of numerous supervised ART algorithms, such as Fuzzy ARTMAP.

      Both the inputs and the targets are clustered in these algorithms, and the two sets of clusters are linked.

      The ARTMAP algorithms' fundamental flaw is that they lack a way to prevent overfitting, hence they should not be utilized with noisy data.

      How Does The ARTMAP Neural Network Work?

      A novel neural network architecture called ARTMAP automatically picks out recognition categories for any numbers of arbitrarily ordered vectors depending on the accuracy of predictions. 

      A pair of Adaptive Resonance Theory modules (ARTa and ARTb) that may self-organize stable recognition categories in response to random input pattern sequences make up this supervised learning system. 

      The ARTa module gets a stream of input patterns ([a(p)]) and the ARTb module receives a stream of input patterns ([b(p)]), where b(p) is the right prediction given a (p). 

      An internal controller and an associative learning network connect these ART components to provide real-time autonomous system functioning. 

      The remaining patterns a(p) are shown during test trials without b(p), and their predictions at ARTb are contrasted with b. (p). 

      The ARTMAP system learns orders of magnitude more quickly, efficiently, and accurately than alternative algorithms when tested on a benchmark machine learning database in both on-line and off-line simulations, and achieves 100% accuracy after training on less than half the input patterns in the database. 

      It accomplishes these features by using an internal controller that, on a trial-by-trial basis, links predictive success to category size and simultaneously optimizes predictive generalization and reduces predictive error, using only local operations. 

      By the smallest amount required to rectify a predicted inaccuracy at ARTb, this calculation raises the alertness parameter an of ARTa. 

      To accept a category or hypothesis triggered by an input a(p), rather than seeking a better one via an autonomously controlled process of hypothesis testing, ARTa must have a minimal level of confidence, which is calibrated by the parameter a. 

      The degree of agreement between parameter a and the top-down learnt expectation, or prototype, which is read out after activating an ARTa category, is compared. 

      If the degree of match is less than a, search is initiated. 

      The self-organizing expert system known as ARTMAP adjusts the selectivity of its hypotheses depending on the accuracy of its predictions. 

      As a result, even if they are identical to frequent occurrences with distinct outcomes, unusual but significant events may be promptly and clearly differentiated. 

      In the intervals between input trials, a returns to baseline alertness. 

      When is big, the system operates in a cautious mode and only makes predictions when it is certain of the result. 

      At no step of learning, therefore, do many false-alarm mistakes happen, yet the system nonetheless achieves asymptote quickly. 

      Due to the self-stabilizing nature of ARTMAP learning, it may continue to learn one or more databases without deteriorating its corpus of memories until all available memory has been used.

      What Is Fuzzy ARTMAP?

      For incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analogue or binary input vectors, which may represent fuzzily or crisply defined sets of characteristics, a neural network architecture is developed. 

      By taking advantage of a close formal resemblance between the computations of fuzzy subsethood and ART category choosing, resonance, and learning, the architecture, dubbed fuzzy ARTMAP, accomplishes a synthesis of fuzzy logic and adaptive resonance theory (ART) neural networks. 

      In comparison to benchmark backpropagation and general algorithm systems, fuzzy ARTMAP performance was shown using four simulation classes. 

      A letter recognition database, learning to distinguish between two spirals, identifying locations inside and outside of a circle, and incremental approximation of a piecewise-continuous function are some of the simulations included in this list. 

      Additionally, the fuzzy ARTMAP system is contrasted with Simpson's FMMC system and Salzberg's NGE systems.

      ~ Jai Krishna Ponnappan

      Find Jai on Twitter | LinkedIn | Instagram

      References And Further Reading:

      • Moreira-Júnior, J.R., Abreu, T., Minussi, C.R. and Lopes, M.L., 2022. Using Aggregated Electrical Loads for the Multinodal Load Forecasting. Journal of Control, Automation and Electrical Systems, pp.1-9.
      • Ferreira, W.D.A.P., Grout, I. and da Silva, A.C.R., 2022, March. Application of a Fuzzy ARTMAP Neural Network for Indoor Air Quality Prediction. In 2022 International Electrical Engineering Congress (iEECON) (pp. 1-4). IEEE.
      • La Marca, A.F., Lopes, R.D.S., Lotufo, A.D.P., Bartholomeu, D.C. and Minussi, C.R., 2022. BepFAMN: A Method for Linear B-Cell Epitope Predictions Based on Fuzzy-ARTMAP Artificial Neural Network. Sensors22(11), p.4027.
      • Santos-Junior, C.R., Abreu, T., Lopes, M.L. and Lotufo, A.D., 2021. A new approach to online training for the Fuzzy ARTMAP artificial neural network. Applied Soft Computing113, p.107936.
      • Ferreira, W.D.A.P., 2021. Rede neural ARTMAP fuzzy implementada em hardware aplicada na previsão da qualidade do ar em ambiente interno.

      AI Glossary - Adaptive Resonance Theory (ART).


      Adaptive Resonance Theory (ART) is a kind of neural network based on neurophysiologic theories.

      Stephen Grossberg came up with the idea in 1976.

      For prediction, ART models use a hidden layer of ideal instances.

      If an input case is sufficiently similar to an existing case, it "resonates" with it, and the ideal case is modified to include it.

      A new ideal scenario is introduced if this is not the case.

      ARTs are sometimes shown as having two layers, known as the F1 and F2 layers.

      The matching is done by the F1 layer, and the outcome is chosen by the F2 layer.

      It's a cluster analysis technique.

      Internet References:


      ~ Jai Krishna Ponnappan

      Find Jai on Twitter | LinkedIn | Instagram

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