假设你尝试将神经网络拟合到从正弦曲线函数采样的数据中。你的网络只有一个输入（相）。哪种神经网络最适合？

Suppose that you are trying to fit a neural network into data that were sampled from a sine-curve function. Your network has only one input (phase). Which neural network is best suited for this?

Question

假设你尝试将神经网络拟合到从正弦曲线函数采样的数据中。你的网络只有一个输入（相）。哪种神经网络最适合？

Suppose that you are trying to fit a neural network into data that were sampled from a sine-curve function. Your network has only one input (phase). Which neural network is best suited for this?

BlackTom AI · Accepted Answer

The problem presents fitting data sampled from a sine-curve function with a network that has a single input (the phase).
Option A: KNN. While K-Nearest Neighbors can interpolate nearby points, it treats data non-parametrically and does not capture temporal dynamics or generalize well to unseen phases, especially for smooth periodic functions.
Option B: GAN. Generative Adversarial Networks are designed for generating realistic data and learning distributions, not for straightforward sequence modeling or function approximation of a simple sinusoid with a single input feature.
Option C: LSTM. Long Short-Term Memory networks are a type of recurrent neural network designed to capture temporal dependencies and retain information over time. For a sine wave, the phase provides a continuous, sequential structure, and an LSTM can learn the underlying periodic pattern, maintaining memory of past phases to predict future values.
Option D: CNN. Convolutional Neural Networks excel at spatial hierarchies and local patterns, but a plain 1D CNN still treats input primarily as local features and may struggle with global periodic structure unless designed with carefully tuned receptive fields; it’s generally less suited for modeling a smooth, continuous time-series with long-range dependencies compared to recurrent architectures like LSTM.
In this context, the goal is to model a continuous, periodic function of phase, where remembering prior phase information improves prediction. LSTM’s memory cells make it the most appropriate choice among the given options for capturing the sinusoidal relationship across the phase input. The other models either lack temporal memory (KNN), are misaligned with the objective of straightforward sequence modeling (GAN), or are less natural fits for a global periodic function (CNN) without substantial adaptation.

2025FallB-X-CSE571-78760 期末考试 Final Exam

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