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neural_network/adaptive_resonance_theory.py

@@ -1,23 +1,23 @@
 """
 adaptive_resonance_theory.py
 
-This module implements the Adaptive Resonance Theory 1 (ART1) model, a type 
-of neural network designed for unsupervised learning and clustering of binary 
-input data. The ART1 algorithm continuously learns to categorize inputs based 
-on their similarity while preserving previously learned categories. This is 
-achieved through a vigilance parameter that controls the strictness of 
+This module implements the Adaptive Resonance Theory 1 (ART1) model, a type
+of neural network designed for unsupervised learning and clustering of binary
+input data. The ART1 algorithm continuously learns to categorize inputs based
+on their similarity while preserving previously learned categories. This is
+achieved through a vigilance parameter that controls the strictness of
 category matching, allowing for flexible and adaptive clustering.
 
-ART1 is particularly useful in applications where it is critical to learn new 
-patterns without forgetting previously learned ones, making it suitable for 
+ART1 is particularly useful in applications where it is critical to learn new
+patterns without forgetting previously learned ones, making it suitable for
 real-time data clustering and pattern recognition tasks.
 
 References:
-1. Carpenter, G. A., & Grossberg, S. (1987). "A Adaptive Resonance Theory." 
-   In: Neural Networks for Pattern Recognition, Oxford University Press, 
+1. Carpenter, G. A., & Grossberg, S. (1987). "A Adaptive Resonance Theory."
+   In: Neural Networks for Pattern Recognition, Oxford University Press,
    pp. 194–203.
-2. Carpenter, G. A., & Grossberg, S. (1988). "The ART of Adaptive Pattern 
-   Recognition by a Self-Organizing Neural Network." IEEE Transactions on 
+2. Carpenter, G. A., & Grossberg, S. (1988). "The ART of Adaptive Pattern
+   Recognition by a Self-Organizing Neural Network." IEEE Transactions on
    Neural Networks, 1(2), 115-130. DOI: 10.1109/TNN.1988.82656
 
 """